Данные и ML вакансии

Location: Philippines; India; Mexico City, Mexico; Mexico; Cairo, Egypt; Brazil; Malaysia; Indonesia Workplace: remote About the Role  We are looking for a detail-oriented QC Reviewer to ensure the quality and consistency of robot manipulation video annotations produced by our annotation team. You'll review labeled video segments against a detailed style guide and reference examples, catch errors or inconsistencies, and provide clear, constructive feedback to annotators. This role is critical to maintaining the accuracy and reliability of training data used for robotics AI models.  What You'll Do      - Review completed annotations of robot manipulation videos across three levels of labeling detail (atomic motion, skill/subtask, task/goal).      - Cross-check annotator descriptions against all three camera views (overhead and both wrist-mounted cameras) to verify accuracy of described actions, object identification, and contact/grasp details.      - Verify full coverage of each video — confirm there are no gaps in labeling, including idle or pause moments.      - Check that descriptions accurately reflect what actually happened in the video, including failures, drops, or slipped grips — flagging any instances where outcomes were inaccurately described or "smoothed over."      - Evaluate annotations for adherence to the style guide: correct vocabulary usage, spatial/directional accuracy, consistent terminology for objects and actions, and appropriate language variation (avoiding repetitive phrasing).      - Identify recurring errors or patterns of confusion among annotators and report them to the team lead for calibration purposes.      - Provide specific, actionable feedback on rejected or revised annotations, referencing the style guide and reference examples.      - Participate in periodic calibration sessions with the team and client to align QC standards with evolving guidelines or edge-case resolutions.      - Maintain a QC log or tracking sheet documenting review outcomes, common error types, and annotator performance trends (as required by the project).    What We're Looking For  Required:      - Excellent written English and strong reading comprehension — you'll be evaluating precise, nuanced language choices, not just checking for typos.      - Exceptional attention to detail — able to catch subtle mismatches between video content and written description (e.g., a mislabeled grasp type, an incorrect object part, a missed failure event).      - Strong grasp of spatial/mechanical description (left/right, above/below, object parts like handles, lids, edges) to verify annotator accuracy.      - Ability to apply a detailed, structured style guide consistently and make sound judgment calls in ambiguous or edge-case scenarios.      - Comfort giving clear, specific, and constructive feedback to annotators (written feedback, not just pass/fail marks).      - Reliable, self-directed work habits, with the ability to work independently while meeting review turnaround expectations.  Nice to Have:      - Prior experience in a QC, editing, proofreading, or annotation review role.      - Experience with video annotation, data labeling, or transcription QA specifically.      - Familiarity with robotics terminology (grippers, end-effectors, manipulation).      - Experience with annotation tools such as Label Studio, including review/approval workflows.  #L1-CC1 Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

Location: Philippines; India; Cairo, Egypt; Mexico City, Mexico; Mexico; Brazil; Malaysia; Indonesia Workplace: remote About the Role  We are looking for detail-oriented annotators to help label robot manipulation videos for AI training purposes. You'll watch short videos of robots performing manipulation tasks (filmed from three synchronized camera angles) and produce precise, structured, natural-language descriptions of the actions taking place. This work directly supports the development of robotics AI models and requires strong written English, sharp observational skills, and the discipline to follow a detailed style guide consistently.  What You'll Do  1. Watch short robot manipulation videos, each filmed from three synchronized camera views (an overhead view and views from each of the robot's two wrist-mounted cameras).  2. Break each video into time segments and write clear, natural-language descriptions for each segment.  3. Apply labels at three levels of detail for each applicable segment:  - Atomic motion (a few seconds) — a single small movement (e.g., "close fingers around the red handle")  - Skill / subtask (several seconds to ~20 seconds) — a complete, meaningful action (e.g., "pick up the red block by its edge")  - Task / goal (up to ~1 minute) — the overall purpose of a sequence of skills (e.g., "place all blocks in the container")    4. Ensure every moment of video is covered by a label at two or more of these levels — no gaps, including idle or pause moments.  5. Accurately describe exactly what happens, including when something doesn't go as planned (a dropped object, a failed grasp, a slipped grip). Precision matters more than making the robot look successful.  6. Cross-reference all three camera angles: use the overhead view to understand the overall scene and object identity, and the close-up wrist views to confirm exact contact and grasp details.  7. Follow a detailed style guide covering vocabulary for actions, spatial relationships, object descriptions, and manner of movement, applying it consistently across many episodes.  8. Participate in periodic calibration sessions to align your labeling with the team and the client's reference examples.    What We're Looking For  Required:  1. Strong written English — you'll write dozens of short, precise descriptive sentences per video and need to vary your language rather than repeating the same phrases.  2. Sharp attention to detail — able to distinguish small differences (a successful grasp vs. a fumble, a push vs. a drag, which specific object part is being touched).  3. Comfort following a detailed, structured style guide and applying it consistently, even in ambiguous or edge-case scenarios.  4. Basic comfort with spatial/mechanical description (left/right, above/below, naming object parts like handles, lids, or edges).  5. Reliable, self-directed work habits — this is often heads-down work with periodic check-ins rather than close supervision.  Nice to Have:  1. Prior experience with video annotation, data labeling, transcription, or QA work.  2. Familiarity with robotics terminology (grippers, end-effectors, manipulation) — helpful but not necessary, as the style guide is self-contained.  3. Experience with annotation tools such as Label Studio.    #L1-CC1 Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

Location: European Union; Ukraine Workplace: Remote ABOUT US Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/ ABOUT THE ROLE We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization. You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse). This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting. You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered. Responsibilities - Design, build, and maintain dbt models (staging, core, marts) based on business needs - Develop scalable SQL transformations and ensure efficient, cost-effective query performance - Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability. - Create and maintain documentation for datasets, data lineage, business logic, and transformation rules - Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness - Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models - Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting - Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization - Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making Requirements - Strong SQL experience with a deep understanding of analytical modeling concepts - Hands-on experience with dbt (models, tests, docs, macros) - Experience working with cloud data warehouses such as BigQuery or ClickHouse - Understanding of data modeling patterns - Familiarity with data engineering concepts (ELT/ETL, orchestration, version control, CI/CD) - Experience with data quality frameworks or implementing validation logic - Ability to translate complex business processes into clean analytical datasets. - Strong documentation skills and attention to detail - Experience collaborating with cross-functional teams and supporting analysts with reliable data - Knowledge of Python is a plus (but not required) Nice To Haves - Experience with orchestration tools (Airflow, Dagster) - Experience working with marketing, product, or billing datasets - Exposure to metric layers, semantic layer design, or BI governance - Basic understanding of observability tooling (Grafana, Prometheus, ... ) - Experience optimizing BigQuery costs or ClickHouse performance LOCATION Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. BENEFITS Discover the perks of being part of our vibrant team! We offer: - Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. - Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. - Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. - Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. - Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! INTERVIEW PROCESS After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: - Recruiter Screening (40 minutes) - Technical Interview (90 minutes) - Final Interview (60 minutes) LIFE AT RUBY LABS At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote

Responsibilities: • Build AI agents from concept to production, including LangGraph workflows, RAG systems, prompts, tool/function calling, external integrations, and streaming LLM applications. • Develop backend services with FastAPI/async, PostgreSQL/SQLAlchemy, Celery/Kafka, and write tests; assess quality and resolve production issues. Requirements: • Strong Python development and practical LangGraph experience. • Experience building LLM applications and RAG systems with embeddings, vector search, relevance assessment, Qdrant, and hybrid/dense/sparse search. • Git, REST, asynchronous backend development, and independent task completion. • Familiarity with LiteLLM/vLLM and local models such as Qwen or Gemma.

Location: London; Europe; Toronto Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role overview: North is Cohere’s AI workspace platform for enterprises: a secure, customizable environment where companies can use AI across their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work, build automations, inspect outputs, and collaborate with AI in production environments. As North becomes more capable, one of the most important questions is also one of the hardest: how do we know whether the model is actually getting better for the workflows customers care about? This role is about being the voice of North inside modelling. You will build the evaluation systems, feedback loops, and applied modelling workflows that make sure model progress translates into better product outcomes for North users. You will work closely with North product teams, customer-facing teams, and modelling teams to define what “good” means across the product surface, turn real usage and product direction into high-quality evals, and use those evals to guide model selection, patches, and regular model updates. This is neither a pure research role nor a conventional product engineering role. It is a rigorous applied MLE role for someone who cares deeply about measurement, model behavior, and real-world product quality. You should be excited by the craft of building careful evals: evals that capture messy agentic workflows, reflect actual customer needs, resist superficial benchmark hacking, and provide useful signal for where the product and models need to go next. Please note: this team works closely across Europe and East Coast North America time zones. We are open to candidates who can collaborate effectively within those hours. Key responsibilities: - Own the eval strategy for North: define what we need to measure across agent workflows, tool use, enterprise knowledge work such as deep research, document creation or editing, and other human-AI interactions. - Build high-quality evals from the realities of the product: user feedback, production failures, privacy-preserving usage logs, internal dogfooding, customer needs, and forward-looking product goals. - Create systems that continuously turn what North is learning from users, customers, and feature teams into evals, so measurement keeps pace with the product rather than becoming a static benchmark. - Be the voice of North inside modelling: extract clear insight from eval results, customer-derived data and product context, then turn model failures, capability gaps, and North-specific needs into actionable recommendations for the central modelling teams. Qualifications: - You have improved LLM-powered, agent-powered, or AI-product systems through evals, feedback loops, data curation, prompting, model adaptation, or model selection. - You care deeply about evaluation as a craft: representative tasks, precise rubrics, clean data, failure analysis, regression tracking, and knowing when a metric is giving false confidence. - You have strong applied MLE judgment and can reason clearly about model behavior, eval validity, product outcomes, and production tradeoffs. - You are comfortable working close to users and product teams, while translating messy qualitative signals into measurement that other modelling teams can act on. - You are self-directed, practical, and motivated by open-ended problems where the right answer requires both technical depth and product understanding. - You care about making AI systems genuinely useful in real enterprise settings, not just better on abstract benchmarks. If some of the above does not line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco Bay Area, USA Workplace: Hybrid WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands.   We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. ABOUT THE ROLE We're looking for exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level. You'll work at the intersection of large-scale machine learning, generative AI, and advertising systems—building the models and infrastructure that determine how creative is generated, ranked, optimized, and ultimately performs. This role is for someone who understands ads systems deeply and is equally strong in modern generative AI. You should be comfortable moving across ranking and recommendation, targeting and optimization, prompt engineering, post-training, and production ML systems. You'll help define what an AI-native advertising platform looks like from the ground up. WHAT YOU'LL DO: - Build and improve ML systems powering advertising products, including ranking, recommendation, targeting, prediction, and optimization. - Develop models that improve ad creative quality, relevance, personalization, and performance at scale. - Build systems that connect generative models with real-world advertising performance signals, creating feedback loops that continuously improve model outputs. - Apply prompt engineering and post-training techniques to improve generative models for advertising and creative use cases. - Work on fine-tuning, preference optimization, evaluation, and other techniques for adapting foundation models to specific creative and advertising objectives. - Design and run experiments across creative generation, ranking, targeting, and delivery to understand what drives advertiser performance. - Build production ML systems that operate reliably at significant scale, from experimentation through inference and serving. - Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into products advertisers can use. REQUIREMENTS - Deep experience building machine learning systems for advertising. - Strong understanding of ads systems, including areas such as ranking, recommendation, targeting, bidding, conversion prediction, creative optimization, or measurement. - Hands-on experience with LLMs, multimodal models, or generative AI systems. - Strong experience with prompt engineering and model evaluation. - Experience with post-training, including techniques such as supervised fine-tuning, preference optimization, reinforcement learning, or related approaches. - Strong software engineering fundamentals and experience shipping production ML systems. - Ability to operate across research and engineering: you can experiment quickly, identify what works, and turn it into a scalable production system. - High agency. - Working English. NICE TO HAVE - Experience building ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform. - Experience with generative video, image, or multimodal models. - Experience using downstream signals such as CTR, CVR, ROAS, engagement, or retention to train or optimize ML systems. - Experience with large-scale model training, inference optimization, or distributed ML infrastructure. - Experience building AI systems that generate or optimize advertising creative. COMPENSATION & BENEFITS • We offer a competitive and thoughtfully structured compensation package designed to align with impact, experience, and long-term growth. • Base Salary: The anticipated salary range for this role is $165 - $230k, depending on experience, skills, scope, and location. • Equity: In addition to cash compensation, employees are eligible to participate in the company’s stock option program and share in Higgsfield’s long-term growth. • • Benefits: We offer a comprehensive benefits package designed to support employees professionally and personally • The opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team. • Significant ownership, direct impact, and opportunities for professional growth as the company scales.   This is a hybrid role based in the San Francisco Bay Area. Team members are expected to work from our San Francisco office three full days per week, with the remaining days worked remotely and are expected to be available during agreed working hours and to maintain sufficient overlap with the relevant team’s time zone. We value in-person collaboration, active communication, responsiveness, and close partnership across teams. Higgsfield AI is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual

Location: Helsinki; Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup. ROLE We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics: - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. PROFILE We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.) - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus - Excellent communication skills in English OUR STACK Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services BENEFITS - Excellent benefits that will depend on the country you're based in

About Us & The Team Til-Qazyna is a national scientific and practical center dedicated to building the core digital and AI infrastructure for the Kazakh language. We develop foundational language resources, linguistic tools, and open-source models, including custom text corpora, morphological analyzers, RAG pipelines, and fine-tuned Large Language Models. We are looking for an AI Engineer to focus exclusively on natural language processing, corpus engineering, and LLM fine-tuning. Key Responsibilities • LLM Adaptation & Fine-Tuning: Fine-tune, evaluate, and benchmark open-source LLMs (Llama, Mistral, Qwen, Gemma) for Kazakh language understanding and generation using parameter-efficient methods (LoRA, QLoRA, PEFT). • NLP & Linguistic Tooling: Develop, maintain, and optimize rule-based and neural NLP components, including morphological analyzers, lemmatizers, tokenizers, and terminology parsers. • Corpus & Data Pipelines: Design automated pipelines for large-scale text crawling, data cleaning, deduplication, synthetic data generation, and dataset curation. • Inference & Deployment: Package NLP models and LLMs into containerized microservices (Docker, FastAPI) and optimize inference using modern serving frameworks (vLLM, Ollama, TensorRT-LLM, ONNX). Requirements • 1+ years of hands-on experience as NLP / AI / Machine Learning engineer. • Solid understanding of the Transformer architecture, attention mechanisms, embeddings, and tokenization. • Experience with core ML/NLP libraries: PyTorch, Hugging Face (transformers, datasets, accelerate, peft), Scikit-learn. • Experience building data preprocessing pipelines and working with structured/unstructured text data. • Practical knowledge of Docker, Linux, and Git. • Degree in Computer Science, Data Science, Applied Mathematics. Nice to Have • Hands-on experience with modern LLM serving and optimization engines (vLLM, TGI, llama.cpp, quantization techniques like AWQ/GPTQ). • Experience with RAG stacks (LangChain, LlamaIndex, ChromaDB, Qdrant, FAISS). • Understanding of the morphological, agglutinative, or grammatical characteristics of the Kazakh language. • Familiarity with alignment techniques (DPO, RLHF) or synthetic data generation. What We Offer • Direct impact on sovereign AI development and language technology used at scale. • Official employment in full compliance with the Labor Code of the Republic of Kazakhstan. • Access to high-performance GPU compute clusters for model training and experimentation.

Location: United Kingdom; Spain; Germany Workplace: remote Help Us Build The Future of Travel At Airalo, we're making it easier for people to stay connected wherever they travel. As the world's first eSIM store, we help millions of travelers access affordable mobile data in 200+ countries and regions around the world. Today, we're a team of 400+ people across 60+ countries, building a product used by travelers every day. We've grown quickly, but we've worked hard to keep what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy. We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone. Your Next Destination Location: Remote. Contract: Full-time, permanent. Benefits: Learn more about our benefits here in this link - https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74  Languages: English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async.   We're looking for a Staff Data Scientist, Pricing to build the analytical and machine learning capability that pricing runs on, from demand modelling through to the systems that recommend price. You won't be starting from zero. We've invested in the foundational data and infrastructure that pricing decisions depend on. What's missing is the capability on top: models the business trusts, experiments that settle pricing questions with evidence, and economics defined once so they hold consistently across Commercial, Product and the customer experience. The arc of the role runs from measurement to prediction to prescription - understanding how demand responds to price, forecasting how it will respond, and ultimately building the models that recommend the price itself. What You'll Do: Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment. Develop machine learning models for demand forecasting and willingness-to-pay, and take them from exploration through to production with the monitoring and retraining that keeps them honest. Move us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints. Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible. Define pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from. Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit. Set the analytical standard for pricing at Airalo, from what counts as evidence through to how a model or recommendation gets validated before it influences live pricing. What You'll Bring: 7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions. Experience with dynamic or algorithmic pricing systems in production. An advanced degree in Econometrics, Statistics, Operations Research or similar, or equivalent applied depth in demand estimation and the identification problems that make naive price-quantity regressions wrong. Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches. Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes. Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly. Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses. The ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome. Genuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision. Fluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain. Excellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors. A self-starter mindset that thrives in ambiguity and brings structure without waiting for permission. Comfort using AI tools to augment analytical work, with a point of view on where they help and where they don't Nice to have: Experience in marketplace, telecom, travel, or subscription/usage-based businesses. Bayesian or hierarchical modelling for sparse segments and long-tail SKUs. Competitive price response modelling, or working with scraped competitor pricing data. Familiarity with dbt, LightDash, or similar semantic/BI layers. Experience designing semantic or metric layers consumed by both analytics and production systems. Experience working in cross-functional teams spanning commercial, finance and product disciplines. If you are interested in this position, please apply via the link. Why People Join Us We started Airalo to make staying connected effortless, wherever you are in the world. Today, millions of travelers rely on our eSIMs, and many of the people building the product were customers first. Our team spans 60+ countries, united by a shared belief that great work happens when people are trusted to own what they do. There's trust to own what you do, space to focus without constant layers of process, and a clear sense of what you're responsible for from day one. As Airalo continues to grow, so do the opportunities to learn, take on new challenges, and make a real impact - from anywhere in the world. Before You Hit Apply By applying, you acknowledge and agree that, in case of successful application, Airalo may request to run background checks as a condition for entering into an agreement with you. Rest assured that these checks will only occur upon your prior consent and at the end of the selection process, and will be strictly limited to what is allowed under the laws that are applicable to you. All data that you share or that we collect in connection with such checks will be processed in accordance with our Privacy Policy, available here: www.airalo.com/more-info/privacy-policy?srsltid=AfmBOooBT0rXAj1FaNelZ3VfN0wvhwzvAoxdtHnOKSVETpiSjiXVuycy    We sincerely thank all applicants in advance for submitting their interest in this opportunity. Airalo is an equal-opportunity employer and values diversity, equity & inclusion. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations upon request for individuals with disabilities throughout our job interview process. At Airalo, we believe in roaming-free recruitment. Just as we provide clear connectivity for travelers, we provide clear salary ranges for our candidates. 🌍 This role is remote and open to candidates across our supported locations in Europe. The range below reflects the full salary range for this role across the countries where it’s available: 📍UK: £93,200 - £139,800 📍Spain: €77,600- €116,400 📍Western/Northern Europe (Germany, France, Austria, Denmark, Sweden, Finland, Norway): €66,400 - €166,800 📍Southern Europe (Portugal, Italy, Greece, Malta): €70,000 - €137,000) 📍Central & Eastern Europe (Czechia, Hungary, Poland, Lithuania, Estonia): €60,000 - €117,000 📍South-Eastern Europe / Balkans (Romania, Serbia, Bulgaria): €56,000 – €100,000 Your Home Destination: 🛫  Local market rates and cost of living vary. Your specific range within this band depends on where you’re based, and there’s room to discuss what’s right for the person and the role. We’ll walk through your range together on your first call with our Talent team so we’re aligned for takeoff! 🎒✨

Location: London Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them. In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues. Key Responsibilities: There are many open problems in this space. As a Member of Technical Staff, RL Environments, you will: - Build new RL environments targeting different agentic capabilities and industry areas - Train and evaluate agents in those environments - Make all the pieces work together: tasks, data, tool implementations, and verifiers - Work across modeling and product to identify gaps in agent performance, and improve both the agents and the environments - Work with external vendors to create high-quality, expert-built RL environments, and build tools to ensure high task, data, and verifier quality - Automate the discovery of model capability gaps, and systematically measure agent performance during evals and training Qualifications: You may be a good fit if: - You have engineered agents and optimized them for specific industry use cases - You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering - You obsess over measuring agentic capabilities and turning that into a repeatable process - You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs - You have designed and run annotation workflows to surface insights into agent performance and verify data quality - You have built synthetic data pipelines to scale eval and training efforts - You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity - A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Обязанности: • разработка и совершенствование End-to-End ML-пайплайнов • разработка продакшен-пайплайнов обработки данных • работа с огромными объёмами данных Сбера (петабайты) на PySpark, исследование подходов применения их в моделях • писать эффективный и масштабируемый код для тренировки и инференса моделей на PyTorch, проводить эксперименты на GPU-кластере • performance оптимизации кода по обработке больших массивов данных или онлайн сервисов рекомендаций с высокой нагрузкой • менторинг младших членов команды, обмен знаниями и экспертизой. Требования: • математический бэкграунд • хорошее знание Python и ключевых фреймворков для работы с данными (PySpark, PyArrow, Pandas) • опыт написания качественного production кода • опыт написания промышленных пайплайнов обработки данных, содержащих множество шагов, зависимостей и сложную логику • опыт использования Airflow (или другого industry-standard оркестраторов пайплайнов, т.к. Luigi, Dagster и т.д.) • хорошее понимание баз данных SQL / NoSQL • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом • опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки. Условия: • комфортный современный офис рядом с м. Кутузовская (опционально) • возможность выбрать удобный график – офис/гибрид/удаленка (опционально) • ежегодный пересмотр зарплаты, квартальная и годовая премия (премия указывается в зависимости от должности и системы премирования) • корпоративный спортзал и зоны отдыха • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития • программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) • расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа • гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ • подписка Прайм с возможностью совместного использования на трёх близких • вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: -Помощь в развитии рекомендательной системы. -Обеспечение производительности и отказоустойчивости runtime и offline-процессов. -Улучшение ML-инфраструктуры. Требования: -Навыки разработки на Java 21 или более поздних версиях. -Понимание микросервисной архитектуры. -Знание алгоритмов и структур данных. -Опыт работы с Git. -Английский язык на уровне чтения технической документации.

Задачи: — Участвовать в формировании модели данных — Проектировать и разрабатывать витрины и потоки данных на основе потребностей бизнес-пользователей — Участвовать в проектах по развитию Data Platform — Писать скрипты SQL и Python (опционально) — Прорабатывать интеграции с внешними системами для Data Platform Требования: — Свободно владеть SQL — Опыт работы с DWH в качестве аналитика или разработчика минимум 5 лет — Понимать теорию СУБД и ETL-процессов — Знание Python или другого языка программирования — Опыт проектирования объектов БД на основании бизнес требований Условия: — Работа в офисе или удаленно по договоренности — Платформа обучения и развития «Т‑Банк Апгрейд» (курсы, тренинги, вебинары, базы знаний) — Поддержка менторов и наставников, помощь в карьерном развитии — Оформление полиса ДМС со стоматологией и страховки от несчастных случаев — Льготное страхование близких — Возможность работы в аккредитованной ИТ-компании — Льготные тарифы на продукты Т‑Банка — Частичная компенсация затрат на спорт — Well-being-программа для улучшения психологического и физического здоровья, а также юридической и финансовой поддержки — Три дополнительных дня отпуска в год — Достойная зарплата, обсуждаемая на собеседовании

Задачи: — Решать сложности бизнес-заказчиков и оценивать полученный результат. — Планировать работу команды (Data Scientists, ML-engineers). — Проектировать развитие системы прогнозирования спроса. — Проектировать с руководителями смежных технических команд совместные интеграции. — Программировать и проводить code review. Требования: — Опыт работы в ML и DS проектах от 5 лет. — Опыт решения задач прогнозирования временных рядов. — Управленческий опыт работы. — Умение писать на Python. — Умение работать с данными на PySpark, SQL или Pandas. — Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг). — Знание Deep Learning фреймворков (Pytorch, Transformers).