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MEDvidi, a US-based mental healthcare platform, is seeking an AI Product Analyst to focus on Voice AI Agents that engage with patients and automate key parts of their care journey. The role involves analyzing AI behavior in production, identifying failure patterns such as hallucinations and inconsistencies, and assessing AI quality and reliability through examination of call transcripts, conversation logs, and operational data. Responsibilities include developing monitoring frameworks and dashboards to track AI performance, detecting anomalies and shifts in quality, and measuring the impact of AI on patient experience and business outcomes. Collaboration with Product, Engineering, QA, and Operations teams is essential. Candidates should have over three years of experience in Product, Data, or Operational Analytics, proficiency in SQL and Python for handling large text datasets, familiarity with ML pipelines or AI products, and an understanding of LLM limitations and AI failure modes. Experience in building monitoring systems and strong communication skills to translate insights into product decisions are required. Fluency in Russian and English (B2+) and availability during CET-overlapping hours are necessary. Preferred qualifications include experience with conversational AI, evaluating LLM outputs, and knowledge of AI quality metrics. The position offers remote work, involvement with impactful AI healthcare products, and benefits such as health insurance and paid leave.

Location: Singapore ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE JOB Mistral AI is seeking an Applied AI Engineer focused on robotics deployment. You will take state-of-the-art AI models and make them work on real robots, in real environments, for real customers. This is a hands-on role at the intersection of machine learning and physical systems: you will own the path from model to deployed autonomy, working alongside robotics engineers, ML researchers, and systems engineers to deliver end-to-end autonomous solutions across diverse use cases. WHAT YOU WILL DO - Integrate AI models with robotic hardware, sensors, and embedded systems, bringing modern perception and language-driven capabilities onto physical platforms. - Improve the robustness, reliability, and safety of robotic systems operating in real-world, unstructured environments. - Test and validate algorithms in simulation and in real-world deployments, moving fluidly between the two. - Analyze field data to improve model performance and system reliability, closing the loop between what happens on the robot and what happens in training. - Bring up, tune, and debug the autonomy stack — localization, mapping, navigation, and perception — on real hardware, from sensor calibration to real-time performance. - Develop and tune robot navigation behaviors — path planning, obstacle avoidance — so systems hold up in dynamic, cluttered, and partially observable environments. - Diagnose failures end-to-end: mapping drift, localization dropouts, perception edge cases, timing issues — and fix them. - Collaborate with robotics engineers, ML researchers, and systems engineers to deliver complete autonomous solutions for customer use cases, from prototype through production deployment. - Build and maintain the tooling needed for deployment, monitoring, and continuous improvement of deployed systems. ABOUT YOU - Bachelor's, Master's, or PhD in Mechatronics, Robotics, or a relevant discipline (e.g. Computer Science, Electrical or Mechanical Engineering). - Fluent in English with excellent communication skills. - Strong software engineering in Python and/or C++: clean, readable, high-performance code; comfortable working in and improving production codebases. - Knowledge of robotics frameworks such as ROS/ROS2. - Experience with robot perception and state estimation: SLAM, localization and mapping, path planning, sensor fusion, or computer vision. - Solid mathematical fundamentals — geometry, probability, and estimation — and the judgment to know when a hand-crafted algorithm beats a learned one. - Familiarity with simulation tools and robotics development environments (e.g. Isaac Sim, Gazebo, or similar). - Strong problem-solving skills and ability to work in interdisciplinary teams. - Comfortable with the messiness of the real world: hardware quirks, edge cases, and field surprises don't scare you. - Low-ego, collaborative, and eager to learn. - Doesn't need roadmaps. Ships. NICE TO HAVE - Experience deploying ML models on embedded or edge hardware (GPU/NPU inference, quantization, real-time constraints). - Hands-on experience with sensor modalities such as LiDAR, depth cameras, or IMUs, including calibration and time-synchronization. - Exposure to VLM/VLA models or other foundation-model approaches for robot perception and control. - Experience with navigation in GPS-denied or otherwise challenging environments, or with visual-inertial odometry. - Working knowledge of established autonomy tooling — e.g. Nav2, SLAM Toolbox, Cartographer, RTAB-Map, or AMCL — and knowing when to build versus reuse. - Experience with behavior trees, dynamic costmaps, or fleet-level navigation. - Contributions to open-source robotics or ML projects. - Experience with safety-critical or regulated deployment environments. - Track record of success through personal projects, professional projects, or academia. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Singapore; Indonesia; Kuala Lumpur, Malaysia Workplace: hybrid We're Osome - an international fintech startup making the lives of entrepreneurs easier. We help thousands of businesses kick admin, accounting and bookkeeping out of their day-to-day, so they can spend more time on what's important to them. We've developed a unique solution that combines SaaS with a human-in-the-loop approach to provide full-fledged services in real-time. We’re experiencing rapid growth in both client base and team, which now comprises around 500 people across our offices worldwide. As we continue to scale, we’re looking for more bright minds who are excited to make a real impact by solving complex problems. What to expect: Accounting in Osome runs as two domain teams. This team, Core, owns the automation and ledger logic behind the numbers: reconciliation, tax computation logic, the ledger itself, and the architecture behind how financial statements get generated . Experience owns everything client- and agent-facing across the whole journey — bookkeeping, accounts review, and tax filing. When we build agent-native access to the ledger and expand our MCP, it will sit here too. This is a systems and architecture-heavy PM role: you'll need to get hands-on enough with data flows, matching logic, and statement structure to work with engineers to make educated architectural calls. This is the work behind reducing ticket volume and turnaround time end-to-end, and a direct expression of our broader product bet that the best support ticket is the one that never gets opened. You'll be designing for two audiences at once — client and agent/ops — who need to move in lockstep. Here's the glance of the role: You'd own: reconciliation, tax computation logic, our proprietary ledger, financial-statement architecture, and agent-native access to the ledger. You'd work with: 2+ dedicated engineers to start, shared design and analytics. The problem to solve: we've taken big steps towards automation, now it comes to making larger leaps. What you'll do: Own the roadmap, priorities, and delivery for reconciliation and ledger/close logic end-to-end. Partner daily with engineers to shape how automation gets evaluated, where it's trustworthy enough to run unsupervised, and where a human-in-the-loop stays the right design. Define the metrics that actually matter here; coverage vs. accuracy trade-offs, not just delivery counts, get them tracked, and use them to decide what's next. Prototype cheaply before committing engineering time; a script, a quick model test, a mocked data flow to test an idea before it's a sprint. Partner closely with Bookkeeping, Accounting, Tax, and Audit ops, internal operations adoption of what teams ship is a huge part of success at Osome. Represent this team's priorities to senior stakeholders, including saying no and getting stakeholders to agree with the reasoning. As the team grows, mentor and eventually help manage other PMs. Who you are: Evaluated against our PM capability framework at the Senior PM bar: Strategic impact : you can define and drive a multi-quarter strategy against an ambiguous, company-level goal, and own the roadmap that follows from it. Domain ownership : you own the business outcome for reconciliation and ledger work, deep enough, fast enough, to be a credible domain expert within two quarters. Problem-solving : you map ambiguous, system-level problems rather than reach for the first plausible fix. Execution : you ship complex, cross-functional work reliably, prototyping to de-risk before committing engineering time. AI-literacy : you're a hands-on AI user beyond prompting: comfortable prototyping with AI coding tools, running your own quick model or evaluation tests, and building enough of a feel for failure modes to make a real call on what's trustworthy. Leadership : ready to mentor at least one other PM as the team grows. What you bring A track record of making good calls under ambiguity, not just executing a well-defined brief. Evidence you've built something from scratch; a prototype, an internal tool, a side project without waiting for someone else to spec it first. Bookkeeping, accounting, or close-process literacy, from product experience in this space, a finance/accounting background, or deep, demonstrable self-study. Comfort partnering closely with engineers on automation- or model-driven work: coverage vs. accuracy trade-offs, evaluation methodology, knowing where full automation isn't trustworthy enough to ship unsupervised yet. SQL or equivalent data fluency. Prior exposure to ledger, tax, or financial-reporting systems specifically. Curiosity about how reconciliation and matching logic work under the hood. Our Benefits Multi-cultural environment to enhance your global perspective. Learning opportunities and mentorship from peers and leaders. Flexible working arrangement. An agile working model focused on goals and performance. Equal Opportunity Statement At OSOME, creating a culture where individuals of all backgrounds feel comfortable really matters. Everyone who applies will receive fair consideration for employment. We do not discriminate based upon race, colour, religion, sex, sexual orientation, age, marital status, gender identity, national origin, disability, or any other applicable legally protected characteristics in the location in which the candidate is applying. We want to ensure that we represent the diversity of talent in the society we live in today. If you have any accessibility requirements that would make you more comfortable during the application and interview process, please let us know so that we can support you.

Location: Sydney Data Support Engineer – Sydney Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager operating across all liquid asset classes worldwide. We are a technology- and data-driven organisation applying a scientific approach to investing. By combining data, research, technology and trading expertise, we foster a collaborative environment that enables us to solve complex challenges. Our culture of innovation drives our ambition to deliver high-quality returns for our investors. Your future role at QRT We are looking for a technically capable Data Support Engineer to join our global Data Science and Analytics function in Sydney. The team is responsible for ensuring that the extensive datasets and production data processes relied upon by QRT’s quantitative researchers, traders and other business users remain accurate, reliable and available when needed. As part of the global support team, you will monitor data processes, investigate production issues and coordinate an effective response when data is missing, delayed or does not meet the expected quality standards. You will work closely with data engineers, researchers, traders and other stakeholders to understand the impact of an issue, identify the appropriate technical owners and keep affected users informed through to resolution. The Sydney position will play an important role in strengthening DSA’s support coverage across the APAC region and bridging the global handover between EMEA and Hong Kong. Your responsibilities will include: Providing first-line technical and data support to quantitative researchers, traders and other data users. Monitoring production data processes and responding to automated alerts relating to failures, delays and data-quality issues. Investigating incidents across data pipelines, including input, transformation and output failures. Validating data for completeness, accuracy, timeliness and expected characteristics. Assessing the potential business impact of data issues and prioritising incidents appropriately. Collaborating with data engineers and other technical teams to diagnose problems and coordinate their resolution. Communicating clearly with affected users and providing timely updates throughout an investigation. Building an understanding of data flows, dependencies, ownership and downstream consumers. Managing support requests and workflows through JIRA, ensuring issues are tracked through to resolution. Contributing to monitoring, alerting, documentation and continuous process improvement. Supporting the onboarding of new team members and helping users understand QRT’s data tools and processes. Contributing to the extension of data coverage, including live data collection and historical backfills. You will gain exposure to a broad range of financial and alternative datasets, including market and reference data, company information, economic data, text-based datasets and other data used to support QRT’s systematic investment activities. Your present skill set A Bachelor’s degree in Computer Science, Data Science, Engineering, Finance or another relevant discipline. Experience in data support, production support, application support or another technically focused support environment. Strong Python skills, including experience using Python to manipulate, investigate and validate data. Experience working with data systems, automated pipelines and APIs. Exposure to large datasets or high-volume production environments. Strong analytical and problem-solving skills, with the ability to investigate issues methodically. Excellent attention to detail and a strong appreciation for data quality and accuracy. Clear communication skills and the ability to work effectively with both technical and business stakeholders. Sound judgement when prioritising incidents, escalating issues and communicating potential impact. Curiosity and a genuine interest in understanding how data processes and systems work. The ability to remain effective in a fast-moving environment where processes, priorities and ownership may evolve. Experience in financial services, quantitative trading or market data would be beneficial but is not essential. Experience using JIRA or a similar workflow-management tool would be advantageous. QRT is an equal opportunity employer. We value diversity as an essential part of our success and empower our employees to work openly and respectfully towards collective goals. Alongside professional development and achievement, we offer initiatives and programmes that support employees in maintaining a healthy work-life balance.

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Video is where multimodal models are weakest and hardest to grade. Temporal reasoning, long-form understanding, grounding events in time, and holding audio, video, and text together do not fall out of image benchmarks — and the evaluations for them are still immature. Closing that gap is gated as much by how we design data and evaluation as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing video and multimodal models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building video understanding, video-language, and video-generation models, as interested in the data behind them as in the models themselves. Video spans two model families judged in completely different ways: models that understand video — answering questions, localizing events, grounding language in time — where the question is whether the answer is correct; and models that generate it, where fidelity, temporal coherence, and physical plausibility matter and no automatic metric is settled. You own the evaluation science for both, and knowing when model-based scoring can stand in for a human versus when it can't. Your conclusions shape what our partners measure and collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates video data for video and multimodal models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of video and multimodal models — video understanding, temporal and event localization, action recognition, long-form video, video-language models, video generation, cross-modal reasoning, and multimodal retrieval and grounding — into concrete data specifications: modalities, annotation schemas, sampling, and evaluation criteria. Build evaluation methodology for video understanding — temporal grounding accuracy, long-context and long-horizon reasoning, and dynamic multi-turn, cross-modal, and retrieval-and-grounding evaluation — clear about when model-based scoring is trustworthy and when a human is needed. Build evaluation methodology for video generation — fidelity, temporal coherence, and physical plausibility, including generative video used as a world model — the regime where automatic metrics are weakest and human judgment matters most. Decide how existing and incoming video should be structured, enriched, and sampled to extract the most model value from it, including from messy, domain-specific footage. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations that surface where video and multimodal systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in video understanding or multimodal ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated video or multimodal models yourself, with strong PyTorch fundamentals. Fluency in the formats and tooling video work runs on: ffmpeg and decord pipelines, temporal and COCO-style annotation, WebDataset, Parquet and Arrow, and HuggingFace datasets. Experience fine-tuning large video or vision-language models with the modern toolchain (HuggingFace transformers, PEFT, efficient inference), and with long-form video, streaming, temporal segmentation, or synthetic video generation. A way of thinking in datasets and benchmarks: you have built evaluation sets, calibrated difficulty, and argued about what makes video data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CVPR, ICCV, ECCV, NeurIPS, or ICLR. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — safety and robustness testing for video and multimodal systems. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Where models actually differ now is robustness across accents, noise, and code-switching; speaker diarization; the naturalness of generated speech; and latency under streaming. Measuring those honestly, and building the data that trains for them, is gated as much by data and evaluation design as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing speech and audio models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building ASR, text-to-speech, speech-to-speech and conversational voice, diarization, and audio-language models, as interested in the data behind them as in the models themselves. Your work is judgment: which conditions and languages a benchmark must cover to be honest, what a transcription convention should be for a given objective, and when an automated metric can be trusted versus when a human ear is required. You will also partner closely with our transcription and linguistics lead, whose standards directly shape what the models learn. What You’ll Own: You will define how Innodata designs, structures, and evaluates audio data for speech and audio models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of speech and audio models — ASR, text-to-speech and speech generation, speech-to-speech and conversational voice, speaker diarization and verification, audio-language models, and streaming systems — into concrete data specifications: modalities, transcription and annotation schemas, sampling, and evaluation criteria. Build evaluation methodology that goes past word error rate — semantic accuracy, robustness to noise and accent, code-switching, diarization error rate (DER), naturalness and intelligibility of generated speech, and streaming latency — and know when automated metrics hold and when they don't. Decide how existing and incoming audio should be structured, enriched, and sampled for coverage that fits the model objective — across languages, accents, and acoustic conditions (studio, real-world, telephonic), speaker demographics, emotional and paralinguistic range, scripted versus spontaneous speech, and single- versus multi-speaker settings, including low-resource and code-switched speech. Partner with the transcription and linguistics lead to turn model objectives into transcription specifications, and to quantify how transcription conventions and quality move ASR and speech-model results. Partner with the audio solutions and engineering team so the audio we collect is built for the model objective: you specify what good data and evaluation require, and they scope programs with customers and capture audio to spec. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations — noisy, accented, and adversarial audio — that surface where speech systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic- and augmented-audio pipeline to turn specifications into operational plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in speech or audio ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals. Fluency in the toolchains and metrics speech work runs on: ESPnet, NeMo, SpeechBrain, or Kaldi, HuggingFace, forced alignment, and WER/CER and the metrics that go beyond them. Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech, and with synthetic or augmented audio (TTS pipelines, noise and room-response simulation). A way of thinking in datasets: you have built evaluation sets, reasoned about coverage across conditions, and argued about what makes speech data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — for example spoofing and voice-cloning robustness, or bias across accents and languages. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Progress in physical AI is gated as much by data design as by model architecture. The best manipulation and locomotion policies, vision-language-action models, and world models are only as good as the trajectories, demonstrations, and environments they learn from — so the highest-leverage decisions are made long before a model trains: what to capture, what to synthesize, how to represent an action, and how to know whether any of it will transfer to a real robot. Innodata is building the data and evaluation practice behind the next generation of robotics foundation models, in direct partnership with the customers and frontier labs defining that frontier. We are hiring a Research Scientist to own the science of that data. You will partner directly with the customers and frontier labs building robot foundation models, as interested in the data those models learn from as in the models themselves. Your leverage is scientific judgment about data: deciding what is worth capturing in the real world versus generating in simulation, how to weight a training mix across embodiments and sensors, and how to measure whether a policy will hold up outside the lab. Your conclusions will shape what our partners collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates data for robot foundation models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of robotics foundation models — vision-language-action models, world models, and manipulation and locomotion policies — into concrete data specifications: modalities, action representations (tokenization, chunking, diffusion and flow-matching action experts), sampling, annotation schemas, and evaluation criteria. Decide what is worth capturing in the real world versus generating in simulation, and curate and weight training mixes across heterogeneous robot datasets that span different embodiments, action spaces, and sensor setups. Guide collection across capture modalities — motion capture, egocentric, exocentric, teleoperation, and multi-sensor — and across synthetic pipelines, so that what we produce maps cleanly to model needs rather than accumulating for its own sake. Build evaluation and benchmarking methodology that predicts real-world transfer — coverage, discriminative power, reliability, and sim-to-real fidelity — including world-model evaluation (rollout quality and action-conditioned prediction) and the domain-randomization and system-identification choices that close the sim-to-real gap. Run the experiments that prove it: fine-tune and evaluate foundation models on Innodata data, with data-quality ablations and scaling studies that demonstrate specific data decisions produce measurable model improvement. Design adversarial and long-horizon evaluations that surface where policies and world models break, and turn those failure modes into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Collaborate with the capture lab, annotation teams, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 4+ years of hands-on industry experience in robot learning or robotics ML. We weight practical experience over formal credentials; because the field is young, a PhD with a compelling, current research agenda can offset the lower end, and we will also consider very senior candidates with the right background. A Bachelor's degree in computer science, electrical engineering, robotics, or a related technical field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated robot policies yourself — manipulation or locomotion via imitation learning or reinforcement learning — with strong PyTorch fundamentals. You build and measure models, you don't just call them. A way of thinking in datasets: you have curated, filtered, and weighted robot data across embodiments and sensors, and you have real opinions about what makes it good for a given objective. Fluency in robotics data formats and standards — the LeRobot dataset format, RLDS, and Open X-Embodiment — along with common motion and sensor formats. Hands-on experience with simulation and synthetic data — NVIDIA Isaac Sim, Isaac Lab, and Omniverse, or MuJoCo and comparable engines — including domain randomization, system identification, and sim-to-real transfer. Experience with teleoperation or egocentric data collection, and comfort adapting VLM backbones for control and fine-tuning large VLA models with the modern toolchain (HuggingFace transformers, PEFT). A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CoRL, ICRA, IROS, RSS, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming for embodied systems, for example safety and robustness testing. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: United States Position Overview Our customers are at the forefront of some of the most interesting data in the world, using SingleStore to push the boundaries every day. To do this well, they leverage the expertise of our Support Engineering Team, composed of technical subject matter experts on the frontlines of critical customer issues. This Support Engineering role skews far more towards engineering than typical technical support as you will need to delve into database internals and memory allocators, investigate Linux and host-related configurations, and diagnose SQL query behavior, all in the name of resolving complex database issues. Our Support Engineers are constantly learning a wide range of customer systems and technical concepts and are thrilled by the new and exciting challenges that they encounter on a regular basis. To accurately identify the source and solution of an issue, this team will take the time to learn about the customer’s business and systems while helping to improve their fundamental SingleStore and database operational knowledge. This often requires additional research and time spent on learning new technologies and tools outside of SingleStore while also being deeply engaged with multiple departments including development teams, query performance engineering, product management, infrastructure SREs, etc. Required Hours: Monday to Friday US West Coast (09:00 AM - 06:00 PM PST) What you'll do: Provide email and live technical support to our enterprise customers, acting as the first line of defense for complex technical issues, bug diagnosis, or resolving critical outages. Identify and reproduce product issues, perform QA, and submit formal bug reports or feature requests to the appropriate Engineering team. Collaborate closely with software developers, sales engineers, and technical account managers to keep all teams up to date with current issues a customer may be facing. Work to streamline and improve various Support and Engineering processes and tools, author new internal run-books or public documentation, and help the team continually improve efficiency and effectiveness. Ensure customers have a positive support experience during every interaction. Your Experience: 4+ years of advanced customer-facing technical support experience. Experience with MySQL or MySQL-like (MariaDB, Aurora, etc) database and SQL Query Optimization. A solid Kubernetes and sysadmin skill set. Also, comfortable testing software and finding its limits, hands-on with Linux, checking utilization of system I/O, measuring network throughput, and are comfortable navigating large log files. Experience with a scripting or coding language. Whether it’s Python, Bash, or Go, you should have a go-to solution for quick and dirty problem-solving and be able to navigate through our code base. Familiarity with distributed systems and AWS/GCP. You understand how organizations deploy and maintain distributed applications and services across multiple machines Bonus points for knowing how to analyze them for performance bottlenecks and remove single points of failure. Strong written and verbal English communication skills. Have the ability to explain technical concepts to a wide range of end-users. About us: SingleStore delivers the cloud-native database with the speed and scale to power the world’s data-intensive applications. With a distributed SQL database that introduces simplicity to your data architecture by unifying transactions and analytics, SingleStore empowers digital leaders to deliver exceptional, real-time data experiences to their customers. SingleStore is venture-backed and headquartered in San Francisco with offices in Sunnyvale, Raleigh, Seattle, Boston, London, Lisbon, Bangalore, Dublin and Kyiv. Consistent with our commitment to diversity & inclusion, we value individuals with the ability to work on diverse teams and with a diverse range of people. T o all recruitment agencies: SingleStore does not accept agency resumes. Please do not forward resumes to SingleStore employees. SingleStore is not responsible for any fees related to unsolicited resumes and will not pay fees to any third-party agency or company that does not have a signed agreement with the Company. SingleStore values individuals for their unique skills and experiences, and we’re proud to offer roles in a variety of locations across the United States. Salary is based on permissible, non-discriminatory factors such as skills, experience, and geographic location, and is just one part of our total compensation and benefits package. Certain roles are also eligible for additional rewards, including merit increases and annual bonuses. SingleStore’s base salary range for this role is $90,000 - $130,000 USD per year For candidates residing in California, please see our California Recruitment Privacy Notice . For candidates residing in the EEA, UK, and Switzerland, please see our EEA, UK, and Swiss Recruitment Privacy Notice. COG00072

Location: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. ABOUT THE ROLE Replit enables people to build software with AI. The infrastructure underneath that experience must make it straightforward to launch services, isolate workloads, and run reliable systems at scale. We're hiring a hands-on Engineering Manager to lead Cloud Infrastructure: the shared infrastructure as code (IaC), networking, storage, compute, and service mesh platforms that Replit's product and platform teams depend on. You'll lead and grow an existing engineering team building and operating these foundations, including Kubernetes, shared edge networking, service mesh and workload identity. This is a platform-building role with production accountability. You should be comfortable going deep on a design or incident while developing a team that does not depend on you for every decision. WHAT YOU'LL DO - Own the cloud-platform roadmap. Lead the team's IaC, networking, storage, compute, and service mesh platforms. Translate product, platform, reliability, and security needs into sequenced outcomes, balancing foundational investment, lifecycle work, and delivery commitments against the team's capacity. - Make infrastructure repeatable and self-service. Build maintained IaC interfaces for services, cells, connectivity, identities, and shared resources. Enable internal customer teams to provision infrastructure without bespoke coordination or dependence on individual experts. - Operate what the team builds. Own platform availability, upgrades, isolation, recovery, and incident remediation. Maintain clear SLOs, sustainable on-call coverage, and primary and backup owners for critical systems. - Stay technically engaged. Review designs and production changes, debug difficult failure modes, and use AI coding tools—including Replit—to prototype and automate. Apply rigorous review and verification to AI-generated infrastructure changes. - Build and grow a high-ownership engineering team. Coach engineers, develop technical leaders, set clear expectations, manage performance, and hire against agreed needs. Delegate meaningful ownership as the team grows. WHAT YOU'LL BRING - Demonstrated engineering management. You have led and developed engineers, made prioritization and performance decisions, hired thoughtfully, and delivered through a team—not only acted as its strongest individual contributor. - Software-oriented infrastructure depth. You have built and operated cloud platforms or distributed systems and can reason across infrastructure code, Kubernetes, networking, service identity, and stateful dependencies. - Safe-change and production judgment. You have owned consequential migrations and incidents, can explain failure modes and rollback limits, and know when simplifying a system is better than adding another platform. - Platform-product and engineering judgment. You understand internal customers, create interfaces other teams adopt, and make clear tradeoffs among reliability, developer autonomy, engineering effort, and workload efficiency. NICE TO HAVE - Experience with multi-tenant, cellular, regional, or dedicated enterprise infrastructure. - Familiarity with GCP/GKE, Terraform or similar IaC systems, Cloudflare, Envoy/Istio, SPIFFE/SPIRE, and managed data services. - Experience with large-fleet rightsizing, infrastructure consolidation, or migrating CI compute without disrupting developer workflows. - A track record using AI tools to increase engineering output while preserving production safeguards. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Location: United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional Research Scientists to join our research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation Identify high-impact gaps in existing benchmark and evaluation literature. Design novel benchmarks in and across STEM fields and on general model functionality. Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training Develop methods for generating high-quality synthetic STEM training data. Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science Research AI systems capable of performing extended scientific and technical work. Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . Demonstrated ability to formulate and execute original research. Strong understanding of modern LLMs and the frontier AI research landscape. Excellent experimental design, quantitative reasoning, and scientific judgment. Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. Excellent technical writing and communication. Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. Build datasets and environments that directly improve the capabilities of advanced AI systems. Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. Apply frontier AI innovations to high-value enterprise workflows. Operate with high autonomy, rapid iteration, and meaningful commercial impact. Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional Research Scientists to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation Identify high-impact gaps in existing benchmark and evaluation literature. Design novel benchmarks in and across STEM fields and on general model functionality. Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training Develop methods for generating high-quality synthetic STEM training data. Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science Research AI systems capable of performing extended scientific and technical work. Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . Demonstrated ability to formulate and execute original research. Strong understanding of modern LLMs and the frontier AI research landscape. Excellent experimental design, quantitative reasoning, and scientific judgment. Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. Excellent technical writing and communication. Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. Build datasets and environments that directly improve the capabilities of advanced AI systems. Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. Apply frontier AI innovations to high-value enterprise workflows. Operate with high autonomy, rapid iteration, and meaningful commercial impact. Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $350,000 OTE + Equity Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional AI Research Scientists to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation Identify high-impact gaps in existing benchmark and evaluation literature. Design novel benchmarks in and across STEM fields and on general model functionality. Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training Develop methods for generating high-quality synthetic STEM training data. Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science Research AI systems capable of performing extended scientific and technical work. Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . Demonstrated ability to formulate and execute original research. Strong understanding of modern LLMs and the frontier AI research landscape. Excellent experimental design, quantitative reasoning, and scientific judgment. Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. Excellent technical writing and communication. Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. Build datasets and environments that directly improve the capabilities of advanced AI systems. Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. Apply frontier AI innovations to high-value enterprise workflows. Operate with high autonomy, rapid iteration, and meaningful commercial impact. Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $150,000 to $300,000 OTE + Equity Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Singapore, Singapore OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa About OKX At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity The Trading Systems team builds the mission-critical order, execution, position, and risk infrastructure that powers OKX’s global trading platform. We operate at the intersection of high performance, correctness, resilience, and scale—where every design decision matters. We are seeking a Senior C++ Software Engineer to lead the design and evolution of our trading counter and core trading systems. This role is for an engineer who combines deep systems expertise with strong ownership: someone who investigates root causes, raises the quality bar across the engineering lifecycle, and turns individual learning into reusable team capability. What You’ll Be Doing Lead the architecture, design, and development of complex, high-performance C++ trading systems. Design reliable distributed systems, including consistency, fault tolerance, disaster recovery, and resilience strategies across system boundaries. Own work end to end—from technical proposals and design reviews through framework refactoring, implementation, deployment, production debugging, replay, and validation. Solve low-level systems challenges involving networking, protocols, concurrency, memory management, performance, and operating-system-level behaviour. Drive architectural improvements by identifying systemic issues and anti-patterns, such as overly coupled or “God Class” designs, and redesigning components with clear responsibilities. Build proactive quality safeguards, including automated testing, failure-injection scenarios, stability validation, monitoring, and replay-based verification. Design controlled experiments to validate architectural assumptions and diagnose complex production issues beyond surface-level symptoms. Work effectively across heterogeneous technology stacks, making sound engineering decisions at language, service, and platform boundaries. Independently explore and apply AI-assisted engineering practices across code understanding, solution design, cross-review, incident analysis, test validation, and knowledge capture. Raise engineering standards through thoughtful code reviews, technical discussions, and continuous improvements to development practices. Mentor engineers and share practical knowledge through documentation, playbooks, technical sessions, and reusable frameworks. What We Look For In You Bachelor’s degree or above in Computer Science, Software Engineering, or a related STEM field; equivalent practical experience is also valued. 5+ years of hands-on software engineering experience, with substantial experience building production C++ systems. Strong experience designing and evolving complex systems, with a clear understanding of architectural trade-offs. Proven knowledge of concurrency, multithreading, network programming, and performance optimisation in Linux-based environments. Hands-on experience with distributed consistency, high availability, fault tolerance, disaster recovery, or other system-level reliability designs. Strong understanding of computer systems fundamentals, including networking protocols, operating systems, memory, storage, and runtime behaviour. A track record of taking ownership proactively, from defining the problem to delivering and validating a robust solution. Exceptional debugging and analytical ability, with a habit of investigating why issues occur—not simply applying short-term fixes. Experience using automated tests, simulation, replay, or comparative experiments to validate system behaviour and reliability. Clear communication skills and a collaborative mindset; able to turn experience into documentation, standards, and practices that help the whole team. Nice to Haves Experience with exchange, brokerage, trading counter, order management, execution, market data, or other low-latency financial systems. Experience with event-driven architectures, messaging systems, or high-throughput distributed platforms. Familiarity with Rust, Java, Go, Python, or other languages used in multi-language production environments. Practical experience applying AI tools to improve software engineering workflows and code quality. Experience working with globally distributed, cross-functional engineering teams. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Location: Taiwan (Remote) Workplace: Remote ABOUT THE OPPORTUNITY We are building a rigorous, verifiable evaluation suite of Terminal-Bench tasks designed to test the limits of large language models on multilingual software challenges. Our goal is to measure multilingual robustness across prompt language effects, non-English data processing, and complex locale/encoding edge cases in terminal workflows. We are seeking experienced native-speaking software engineers to design, build, and validate these benchmarks. You will create high-signal, high-quality tasks that genuinely test a model's ability to handle multilingual environments without relying on English translation crutches. Note this is a remote, freelance opportunity WHAT YOU’LL DELIVER - Task Engineering: Evaluating Coding Agents. - Asset Creation: Build realistic task environments using datasets and files in your native language. Crucially, these assets must remain in the target language to genuinely measure multilingual handling. - Prompting & Translation: finding failure points where AI does not work, in your native language - Implementation & Verification: Support the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary). - Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Sonnet, Opus). - Quality Assurance: Participate in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity. QUALIFICATIONS - Experience: 1+ years of industry experience in software engineering. - Background: Proven track record at leading technology companies and/or graduation from top-tier engineering universities. - Language: Native or near-native fluency, with a deep understanding of its grammar, register, and phrasing rules. High English proficiency. - Technical Stack: Strong proficiency in Python, standard shell scripting, and data processing. - Workflow: Extensive experience with Terminal/CLI-based development workflows and a working familiarity with coding agents. - Domain Expertise: Deep technical understanding of multilingual text processing pitfalls, including: - Encoding/decoding robustness and Unicode normalization. - Locale-dependent conventions (collation, casing, non-Gregorian dates). - Text I/O, toolchain interoperability, and safe string operations. - (For specific languages) Bidirectional/RTL handling, font fallbacks, and rendering/typography in UI or artifacts. WHY COLLABORATE WITH LILT? - Your schedule, your rules. As an independent contractor, work when you want, as much or as little as you want. No fixed hours, no check-ins, no micromanaging. - Get paid quickly and fairly. We respect your time and your expertise. Competitive rates, prompt payments, no chasing invoices. - Work on projects that actually matter. Contribute to cutting-edge AI and language technology that is shaping how humans and machines communicate. - Be part of something bigger. Join a global community of linguists, subject matter experts, and language professionals who are advancing human knowledge together. - Grow without limits. As a Lilt contractor you get access to diverse, innovative projects that expand your portfolio and sharpen your skills across industries and domains. - Have fun doing what you love. Bring your language skills to life on projects that are as interesting as they are impactful.We are building a rigorous, verifiable evaluation suite of Terminal-Bench tasks designed to test the limits of large language models on multilingual software challenges. Our goal is to measure multilingual robustness across prompt language effects, non-English data processing, and complex locale/encoding edge cases in terminal workflows. WHAT TO CONSIDER BEFORE APPLYING - Not ideal as a full time job or primary income source. Work availability fluctuates with project demand, making this better suited as a supplemental income stream. As a 1099 contractor, you won't receive benefits such as health insurance, paid time off, or retirement contributions, and hours are not guaranteed. - Requires reliable availability and commitment. Once you accept a task, we expect quality work and on-time delivery. Most tasks require a minimum of 2 hours per day or 15-20 hours per week. If your schedule is unpredictable, this may not be the right fit. - Geographic restrictions may apply. We cannot engage contractors in regions subject to international embargo or sanctions. As a 1099 contractor, you are solely responsible for your own tax obligations. We recommend consulting a tax professional before engaging. HOW TO JOIN OUR EXPERT COMMUNITY 1 - Submit your application including an updated copy of your CV in English 2 - Next, complete a GenAI assessment to evaluate your skills 3 - Finalize onboarding and profile set-up in our system, and become eligible for Applied AI projects. AI is changing how the world communicates — and LILT is leading that transformation. LILT's mission is to make the world's information available to everyone, no matter the language they speak. Join our global community who thrive on innovation and excellence. Our collective knowledge, uniqueness, and skills deliver multilingual AI and human-verified services to Enterprises, Governments, and AI Developers around the world. Earn money. Have fun. Advance human knowledge. Work on diverse projects from anywhere, any time you want. Get paid quickly and fairly, and build your professional network in a supportive community—all through a streamlined application process tailored to your expertise. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy. At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.

Location: Hong Kong (Remote) Workplace: Remote ABOUT THE OPPORTUNITY We are building a rigorous, verifiable evaluation suite of Terminal-Bench tasks designed to test the limits of large language models on multilingual software challenges. Our goal is to measure multilingual robustness across prompt language effects, non-English data processing, and complex locale/encoding edge cases in terminal workflows. We are seeking experienced native-speaking software engineers to design, build, and validate these benchmarks. You will create high-signal, high-quality tasks that genuinely test a model's ability to handle multilingual environments without relying on English translation crutches. Note this is a remote, freelance opportunity WHAT YOU’LL DELIVER - Task Engineering: Evaluating Coding Agents. - Asset Creation: Build realistic task environments using datasets and files in your native language. Crucially, these assets must remain in the target language to genuinely measure multilingual handling. - Prompting & Translation: finding failure points where AI does not work, in your native language - Implementation & Verification: Support the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary). - Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Sonnet, Opus). - Quality Assurance: Participate in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity. QUALIFICATIONS - Experience: 1+ years of industry experience in software engineering. - Background: Proven track record at leading technology companies and/or graduation from top-tier engineering universities. - Language: Native or near-native fluency, with a deep understanding of its grammar, register, and phrasing rules. High English proficiency. - Technical Stack: Strong proficiency in Python, standard shell scripting, and data processing. - Workflow: Extensive experience with Terminal/CLI-based development workflows and a working familiarity with coding agents. - Domain Expertise: Deep technical understanding of multilingual text processing pitfalls, including: - Encoding/decoding robustness and Unicode normalization. - Locale-dependent conventions (collation, casing, non-Gregorian dates). - Text I/O, toolchain interoperability, and safe string operations. - (For specific languages) Bidirectional/RTL handling, font fallbacks, and rendering/typography in UI or artifacts. WHY COLLABORATE WITH LILT? - Your schedule, your rules. As an independent contractor, work when you want, as much or as little as you want. No fixed hours, no check-ins, no micromanaging. - Get paid quickly and fairly. We respect your time and your expertise. Competitive rates, prompt payments, no chasing invoices. - Work on projects that actually matter. Contribute to cutting-edge AI and language technology that is shaping how humans and machines communicate. - Be part of something bigger. Join a global community of linguists, subject matter experts, and language professionals who are advancing human knowledge together. - Grow without limits. As a Lilt contractor you get access to diverse, innovative projects that expand your portfolio and sharpen your skills across industries and domains. - Have fun doing what you love. Bring your language skills to life on projects that are as interesting as they are impactful.We are building a rigorous, verifiable evaluation suite of Terminal-Bench tasks designed to test the limits of large language models on multilingual software challenges. Our goal is to measure multilingual robustness across prompt language effects, non-English data processing, and complex locale/encoding edge cases in terminal workflows. WHAT TO CONSIDER BEFORE APPLYING - Not ideal as a full time job or primary income source. Work availability fluctuates with project demand, making this better suited as a supplemental income stream. As a 1099 contractor, you won't receive benefits such as health insurance, paid time off, or retirement contributions, and hours are not guaranteed. - Requires reliable availability and commitment. Once you accept a task, we expect quality work and on-time delivery. Most tasks require a minimum of 2 hours per day or 15-20 hours per week. If your schedule is unpredictable, this may not be the right fit. - Geographic restrictions may apply. We cannot engage contractors in regions subject to international embargo or sanctions. As a 1099 contractor, you are solely responsible for your own tax obligations. We recommend consulting a tax professional before engaging. HOW TO JOIN OUR EXPERT COMMUNITY 1 - Submit your application including an updated copy of your CV in English 2 - Next, complete a GenAI assessment to evaluate your skills 3 - Finalize onboarding and profile set-up in our system, and become eligible for Applied AI projects. AI is changing how the world communicates — and LILT is leading that transformation. LILT's mission is to make the world's information available to everyone, no matter the language they speak. Join our global community who thrive on innovation and excellence. Our collective knowledge, uniqueness, and skills deliver multilingual AI and human-verified services to Enterprises, Governments, and AI Developers around the world. Earn money. Have fun. Advance human knowledge. Work on diverse projects from anywhere, any time you want. Get paid quickly and fairly, and build your professional network in a supportive community—all through a streamlined application process tailored to your expertise. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy. At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. About the role As a Staff Backend Engineer, you will provide technical leadership across your team and adjacent teams, solving the highest-scope and most complex problems in your area. You will lead large, cross-cutting backend initiatives, drive our modular architecture strategy, and define the standards that let teams move faster without compromising quality, security, reliability, or operability. This is a technical leadership role that combines deep backend expertise, systems judgment, product judgment, and influence across organizational boundaries. You will work with Product, Frontend, Infrastructure, Security, Data, Engineering Managers, and engineers across product groups, bringing subject matter expertise, and you will do it without relying on formal authority. Why you’ll love this role Leverage: Your work multiplies across teams. The platforms, standards, and architectural decisions you lead determine how quickly everyone else can ship. Genuinely Hard Problems: You’ll lead the modernization of a large monolith toward modular, reusable, appropriately independent systems — at the scale of a platform used by millions of users. Influence Without Bureaucracy: You’ll shape technical direction across teams through clear written proposals and decision records, and partner with Product and Engineering leadership on platform roadmap and investment trade-offs. Frontier AI Work: You’ll lead adoption of AI-assisted and agentic engineering workflows and define the review, quality, security, and privacy guardrails that make them safe to rely on. Autonomy: You’ll thrive in our remote-first, asynchronous organization, where clear written communication and self-direction are the pillars of success. What you’ll do Lead Cross-Cutting Initiatives: Lead the technical design and delivery of large backend initiatives that span teams, and identify and remove systemic bottlenecks across product and infrastructure groups. Own Architecture Strategy: Drive modular backend, service, data, and platform architecture. Lead modernization of the monolith and legacy backends toward modular, reusable, and appropriately independently operated systems — while preventing fragmentation and service sprawl. Set the Standards: Define and improve standards for APIs, contracts, compatibility, schema evolution, testing, security, performance, observability, operability, and maintainability — plus the build, test, deploy, release, rollback, and production-readiness practices needed to operate modular features safely. Establish Shared Patterns: Create the patterns for dependency management, data ownership, eventing, failure isolation, service maturity, and operational documentation. Establish Go service patterns and Kubernetes operating practices where they matter to our production estate, and guide Rust adoption where it solves a real systems, security, performance, or supply-chain problem. Own What You Build in Production: Take part in on-call and incident response for the systems your teams own, act as a technical escalation point during complex incidents, and turn what you learn into architectural and operational improvements that prevent recurrence. Influence and Partner: Shape technical direction across teams without formal authority, and partner with Product and Engineering leadership on platform-level roadmap decisions and investment trade-offs. Grow Engineers: Mentor Senior and Intermediate engineers, shepherd technical debt reduction and reusable patterns that improve throughput, and raise the technical capability of the wider team. Lead AI Adoption: Lead team-level adoption of AI-assisted and agentic workflows, including the quality, security, privacy, and operational guardrails, and help engineers direct, validate, reject, and course-correct agent output. On-Call Support: Take part in on-call or operational support where your team’s ownership model requires it. What you’ll bring Cross-Team Technical Leadership: A sustained record of technical leadership across more than one team or a broad product area. Deep Architecture Experience: Deep backend architecture and production experience in complex applications, services, or platforms, including scaling or governing APIs, service platforms, data systems, modular monoliths, or shared backend components. Modularization in Production: Experience modernizing a monolith or legacy backend architecture in production, and with modular architecture such as bounded contexts, package encapsulation, ports and adapters, service extraction, event-driven boundaries, or an equivalent approach. Influence Without Authority: A strong ability to create alignment through clear written proposals and decision records, so decisions are documented, understood, and adopted without constant escalation. Product Judgment: The ability to balance customer outcomes, delivery speed, technical health, reliability, and long-term platform investment. Mentorship: Experience mentoring engineers and shaping standards through code review, documentation, and example. Agentic AI Leadership: Production experience with AI-assisted or agentic engineering workflows, including the ability to evaluate and improve how a team adopts them. Helpful experience Go services and APIs at scale. Kubernetes and cloud-native operations, including deployment, scaling, observability, rollback, and production readiness. Rust adoption or production ownership for systems, security, performance, or platform components. Ruby/Rails modularization and large-monolith modernization. PostgreSQL scaling, data partitioning, queues, messaging, or distributed systems. Security-sensitive product or software supply chain engineering. Experience across SaaS, single-tenant, self-managed, or air-gapped deployment constraints. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Poland; Georgia; Serbia Workplace: Remote About Samsung Food (Whisk) At Samsung Food (you might know us as Whisk), we are pioneering the future of everyday care and health — helping individuals and families around the world live better through connected, proactive, and personalized experiences. Our mission is to connect food, health, and home across the Samsung ecosystem, turning data from your devices into personalized guidance that supports you and your loved ones. We work at the intersection of AI, nutrition, behavior change, and digital health. The Samsung Food app was included in Google Play’s Best of 2020 Everyday Essentials list, has been regularly featured on the Apple App Store, and was nominated for a 2021 Webby Award. We’ve also launched Vision AI, which recognises food and meals using our global Food Genome, and developed deep integrations with Samsung Health and SmartThings. We are a remote-first team of more than 100 people in over 30 countries, united by our commitment to innovation and impact. As part of Samsung Electronics, we combine the agility of a startup with the scale of the world’s largest consumer electronics company. Join us and help build technology that improves daily life for tens of millions of people across phones, wearables, TVs, and kitchen appliances in the years to come. Learn https://samsungfood.com/working-at-samsung-food/ more about how we’ve shaped a high-performing global team over the past 14 years. ABOUT THE ROLE Samsung Health helps hundreds of millions of people understand their health, through the devices already in their hands. We're looking for a Staff Product Manager to lead the product definition behind a new AI coaching capability; the end to end experience, coaching logic, the behaviour model, and support the engine build and the practice for evaluating whether the coaching is genuinely good. The problem takes real judgment: coaching that arrives at the few moments that actually matter, and stays quiet the rest of the time. You'll own that judgment layer. This is a senior individual contributor role. You won't have direct reports, though other team members will look to you for day to day direction. You'll report to the Head of Product and work across teams in Europe, Asia and North America. WHAT YOU'LL DO - Own the coaching experience. Start user first, and ensure the behaviour model and the decision logic serve what it feels like to be coached and determine what a coach says and, more often, when it stays quiet. - Make decisions leave the room. Several organisations across several time zones build against what you define. Your job is to produce things others can act on without you present. Not documentation; thinking made buildable. - Own the seams. Multiple interfaces across organisational boundaries. Contracts, schemas, who writes what, what happens when a signal doesn't arrive. - Hold the evidence for coaching quality. Accuracy is the easy half. Helpfulness, personalisation, and whether a coaching moment feels like support are the hard half. You'd apply our thinking, extend it where it's thin, and own the evidence that the coaching is actually good. - Decide what ships and what waits. Including what a first release is allowed to be wrong about, and what it isn't. WHAT YOU BRING You've taken something from undefined to launched. A product that reached real users while accountable for the outcome. You've invented a definition from an ambiguous starting point and can demonstrate the artefact you created to do it. Product craft - You go into the material. You write, sketch and prototype your way to a definition rather than arriving with one. The brief describes what you found; it isn't the first thing you produce. - You feel where an experience fails before the data says so - and you can name why it fails as a principle, not a preference. - You author coherence. You can take a pile of features, screens, models, and constraints and turn it into one thing that feels intentional beyond a sum of parts. - You treat AI product failures as context problems before model problems. What information reaches the model, when, in what form, and the minimum that produces an acceptable answer rather than the ideal one. - You've killed something that worked. A feature that functioned, tested fine, and still wasn't earning its place. Knowing what to cut is part of product judgment. How the work travels - Your writing does work. Specs, one-pagers, decision notes; these are how you think, not what you produce after thinking. Your artefacts survive being re-told into other organisations without you in the room. - You start before the box is drawn. You don't wait for requirements, but you don't build blindly either. You'll decide at 60%, name the risk, and correct as needed. - You anticipate what another organisation will need. Part of this job is being read correctly by people who may not sit in your meetings, in a different location, in a different working language. - You've built ambitiously inside real constraint. A platform owner, a regulator, a parent company, an enterprise partner. You treat constraint as material to design with rather than a reason things don’t work. - You've worked more than one way, and you know which way fits. Discovery, delivery, evaluation, decision-making, practised in different company shapes, so you can say why a method suits this situation.

Location: Williston, United States GoMining is one of the largest Bitcoin mining ecosystems in the world, serving millions of users globally. We run our own industrial-scale mining infrastructure across the United States — a fleet of more than 80,000 machines — and we keep expanding. We're looking for an experienced ASIC Repair Engineer who can diagnose and repair Bitcoin mining equipment at the component level, keeping that fleet running at maximum uptime. Relocation support available! We welcome candidates from across the U.S. and provide a relocation package for those moving closer to one of our data center locations. Responsibilities Diagnose and repair ASIC miners, including Bitmain and MicroBT equipment Troubleshoot and repair hashboards, control boards and power circuits at component level Diagnose chip failures, voltage irregularities and board-level defects Perform SMD and BGA soldering and component replacement Configure, test and commission repaired equipment Keep repair records, inventory updates and technical documentation Work with site technicians and site management to keep fleet uptime high Support preventive maintenance and hardware optimization Requirements 3+ years repairing Bitcoin mining equipment at component level Hands-on experience with Bitmain miners (S19 XP , S21, T21 or similar) Hands-on experience with WhatsMiner equipment (M30++, M50 series or similar) Strong understanding of ASIC miner architecture and diagnostics Practical SMD and BGA soldering skills Ability to read schematics and technical documentation Solid knowledge of electronics, circuit design and power systems Experience at a mining farm, service center or high-density data center is a strong plus Benefits Professional growth: support for courses, conferences. Paid leave: up to 20 vacation days, 15 sick days, plus holidays. Recognition programs: structured performance reviews and team awards. 401k benefits. $108,000–$240,000 per year A strong fixed base plus performance pay tied to the work you complete Our engineers average around $180,000 a year that's the middle of the team, not the top of it. With a fleet this size, the bench is always full Relocation package available