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Location: Berlin Workplace: Hybrid adjoe builds the technologies behind mobile app growth and monetization, challenging the status quo of adtech by redefining how apps acquire users and generate revenue. Our two products, Playtime Arcade and adjoe Ads, sit at opposite ends of the mobile advertising spectrum: rewarded engagement and programmatic in-app advertising. Together they run on 13% of the world's smartphones, connecting advertisers, publishers, and hundreds of millions of users, with one goal: making mobile advertising work better for everyone in the ecosystem. Next-Gen Machine Intelligence for Scalable Discovery & Engagement The Data Science Research team builds foundational ML representations and advanced AI capabilities that enhance both Playtime and adjoe Ads. Operating as a horizontal deep-tech layer, the team leverages advanced AI and deep learning approaches—specifically generative approaches —to deliver critical systems such as high-dimensional user embeddings, general content understanding and sequential recommendation. These frameworks scale adjoe's products beyond standard industry limitations, utilizing behavioral and transactional data from over 770 million users. The team operates at the intersection of bleeding-edge R&D and high-scale production engineering, acting as the company’s internal science bar raiser and primary consulting layer for complex ML bottlenecks. The stack is optimized for deep learning and heavy data throughput: PyTorch and [Ray/Hugging Face] for training and fine-tuning foundation models, Trino and Athena for querying massive user-behavior data lakes, and dedicated GPU infrastructure for heavy workloads. The full lifecycle of these AI capabilities is owned by the team—from academic paper translation and offline prototyping to deploying core algorithmic frameworks built to sustain high-throughput, realtime production environments. Your Mission & Who We Are Looking For: - PhD or equivalent research depth, with a production track record. You have a PhD in CS, Statistics, Math, or Physics, or an MSc with extensive industry research, and publications in top-tier venues like SIGIR, KDD, NeurIPS, or ICLR. You've spent 4+ years building large-scale recommender systems in high-throughput environments: AdTech, e-commerce, or social. - You translate research into production, not just prototypes. You distill SOTA research into robust, production-ready code optimized for sub-millisecond latency. At adjoe, that means architecting multi-stage recommendation pipelines, Generative Retrieval, Deep Ranking, and Re-ranking, serving millions of requests across a stack built on 770 million users' behavioral data. - Deep expertise in PyTorch and large-scale model optimization. You have hands-on experience with high-concurrency optimization, model quantization, and distillation, the engineering side of getting large generative models to run under extreme production constraints, not just in research environments. - Content understanding and embeddings are core to your work. You research and develop information-rich user and ad embeddings using unsupervised and generative approaches, directly improving the quality of predictions across both Playtime and adjoe Ads. - Raises the technical bar around them. You lead architectural debates, mentor junior scientists, and drive the team's research direction without needing a roadmap handed to you. 🌎 We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to join our team. What’s in It for You? At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech. The systems you work on will reach hundreds of millions of users and power billions of decisions every day. - Go Big. Own projects with impact on 770M users and push adtech boundaries. - Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum. - Be Direct. Solve problems openly and collaborate across teams. - Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other. - Have Fun. Celebrate wins, enjoy daily victories, and bring your energy. We welcome applications from people who will contribute to the diversity of our company.

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: US - Headquarters - Maryland - Columbia Who is Tenable? Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey! What makes Tenable such a great place to work? Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together! Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment. This includes, but is not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc Your Role: As an AI Security Engineer on Tenable's Information Security team, you will help shape and mature our approach to securing AI both within Tenable's products and across the enterprise. AI introduces a new class of risk at the intersection of application security, cloud risk, and data governance, and you will be on the front lines of defining how we assess, test, and address it. You will apply hands-on AI engineering experience alongside deep product security and application security skills to design security controls for AI systems, lead assessments, and help engineering teams ship AI features securely. You will establish security standards for responsible AI use, influence decisions across the organization, and build tooling that raises our security posture. This role is ideal for a practitioner who has spent real time building with AI systems, looking under the hood of model internals, and wants to channel that experience into a security specialty. Your Opportunity: Design, implement, and maintain end-to-end security controls across the AI/ML lifecycle. Conduct safety research, inspecting model internals (e.g., detecting hidden deception, latent knowledge, or unwanted concepts across layers) as a core methodology. Perform AI safety assessments and threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. Design and implement robust security guardrails, controls, and boundary mechanisms for Large Language Models (LLMs), Small Language Models (SLMs), and open-source models. Act as a subject matter expert in AI safety research by inspecting model internals to detect hidden deception, latent knowledge, or unwanted concepts, ensuring highly transparent and aligned AI outputs. Continuously monitor the AI landscape for novel vulnerabilities and attack techniques, applying deep engineering experience to proactively defend enterprise AI deployments. Develop security reference architectures for AI deployment patterns, including MCP servers and agentic AI workflows and harnesses. Deploy controls to ensure model integrity, governance, and proper access control across models and feature stores. Build AI-driven tooling to strengthen cybersecurity posture across identity, incident management, vulnerability management, third-party risk, and emerging AI threat vectors. Perform AI safety, security assessments, threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. Collaborate with software engineers, and product teams to integrate security best practices into AI development, training, validation, and deployment processes. Drive Secure Software Development Lifecycle (SSDLC) and DevSecOps practices for AI-powered products, including threat modeling, security testing, penetration testing, and CI/CD pipeline security automation. Create security guidance, documentation, and training for internal engineering and development teams; develop metrics that drive desired security behaviors and outcomes. Research emerging trends in AI security and contribute to innovative solutions that support Tenable's AI initiatives. What You'll Need: 5 or more years of professional experience in information security or application security, with at least 1–2 years focused on securing AI/ML systems. Master’s Degree in Computer Science, Cybersecurity, Data Science, or a related field preferred; advanced degree preferred. Deep understanding of AI-specific security threats, including adversarial ML, data poisoning, prompt injection, model inversion, model evasion, and inference attacks. Strong coding experience in Python and/or Go. Experience with frameworks & libraries such as PyTorch, Hugging Face Transformers, TensorFlow or similar. Experience using specialized interpretability and probing tools/libraries such as TransformerLens, NNsight, Captum, or LIT (Learning Interpretability Tool). Familiarity with mechanistic & Architectural Concepts: inspecting internal model states, residual streams, attention patterns, activation steering, Sparse Autoencoders (SAEs), or feature attribution. Strong understanding of the ML/AI lifecycle and the security risks associated with each stage. Knowledge of cloud security platforms: AWS, Azure, or GCP including AI/ML-related services. Knowledge of data security principles, including encryption, masking, and tokenization. Knowledge of SSDLC, DevSecOps, and security testing practices, including SAST, DAST, SCA, and threat modeling. Knowledge of application security architecture best practices and patterns, including modern web applications, Docker, and microservices. Ability to work cross-functionally across engineering, product, and business teams. Strong written and verbal comAAmunication skills; able to clearly translate complex AI security risks for both technical and non-technical audiences. Strong problem-solving skills, attention to detail, and ability to lead projects with end-to-end ownership. Self-motivated and effective working independently and across distributed teams. And Ideally: Hands-on experience with AI red teaming, adversarial prompt testing, or LLM security assessments. Familiarity of AI security frameworks and standards, including OWASP LLM Top 10 and the NIST AI Risk Management Framework. Experience with application security assessment tools (Burp Suite, ZAP, Tenable WAS, etc). Familiarity with AI governance frameworks (EU AI Act, NIST AI RMF, etc.). Background in cloud security or large-scale SaaS product environments. Security certifications: CISSP, CSSLP, SANS GIAC, CEH, or AI-focused security certifications are a plus, but not necessary. #LI-Hybrid #LI-MS1 This is the base pay range for this position. Compensation for the role will depend on a number of factors, including the candidate's qualifications, skills, competencies, location and experience, and may fall outside of the range shown. Employees are also eligible for variable compensation in addition to base pay (commission for sales roles, bonus for non-sales roles), depending on company and individual performance. Tenable also offers a variety of comprehensive and competitive benefits which include: medical, dental, vision, disability and life insurance; 401(k) retirement savings with company match; an employee stock purchase plan; an employee referral program; flexible spending accounts; an Employee Assistance Program (EAP); education assistance; parental leave; paid time off (PTO); company-paid holidays; health and wellness events; and community programs. US Pay Range $122,500 — $163,166.67 USD We’re committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels.  If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact Recruiting@Tenable.com for further assistance. Tenable Data Consent Statement Tenable is committed to protecting the privacy and security of your personal data. Depending on your location, one or more of the following notices may apply to you: General Applicant Privacy Notice California Applicant Privacy Notice EU/EEA/UK Applicant Privacy Notice

Location: Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Amsterdam, Netherlands; Berlin, Germany; Israel; Remote - Europe About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Role Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. We're looking for a Senior Computer Vision Engineer to join our Embodied AI team, focused on 3D scene understanding and semantics. A reconstructed environment is only useful to a robot once it knows what is in it: which surfaces are floor, which objects can be grasped, moved, or opened, where one room ends and another begins. You'll work hand in hand with our Research Scientists to take advances in open-vocabulary perception, 3D semantic labeling, and scene graph construction and turn them into reliable capabilities that embodied AI teams can use in production. This is an applied research role. You'll live at the boundary between a research prototype and a production pipeline: adapting methods so they hold up on messy customer captures, lifting 2D semantic signals into geometrically consistent 3D, defining what "semantically correct" means for a downstream policy, and feeding what you learn back into the research agenda. You understand that the difficult part is often not the core method, but the messy inputs, edge cases, operational constraints, and downstream requirements surrounding it. You care about quality, cost, latency, and reliability, and you know that a system is only successful when people can depend on it. This is a hands-on engineering role for someone who wants to build, ship, and own outcomes. You'll help shape both the technology and the product, while remaining close to the code and the problems our customers are trying to solve. What You'll Do - Productionize 3D Scene Understanding Capabilities - Turn research prototypes in open-vocabulary segmentation, 3D semantic and instance labeling, affordance prediction, and scene graph construction into reliable pipeline components and services that operate across varied customer data and environments. - Own End-to-End Output Quality - Ensure that reconstructed environments carry labels, instances, and relationships that are consistent, complete, and correct enough for downstream simulation and policy learning, and define what "correct enough" means for each customer workflow. - Bridge Research and Production - Work day to day with the Embodied AI Research Scientist: pressure-test new methods on real customer data, identify where they break, and bring production and customer constraints back into research planning to prioritize the advances that matter most. - Make Systems Robust to the Real World - Diagnose failures caused by capture quality, scene complexity, scale, calibration, coordinate conventions, and other assumptions that prototypes often leave implicit. - Build Evaluation and Quality Infrastructure - Create datasets, regression tests, quality gates, and benchmarking tools that help the team measure whether changes improve the system. - Improve Performance, Throughput, and Cost - Profile and optimize GPU and distributed workloads, reduce unnecessary reruns, and help establish the economics of running reconstruction at scale. - Work Across the Product Boundary - Partner with Embodied AI product and engineering teams to understand customer requirements and deliver capabilities that fit real training, evaluation, and deployment workflows. Help evolve the representations, tooling, and operational systems that allow reconstructed environments to move reliably into customer applications. What You'll Bring - 5+ years of computer vision experience and a bachelor's degree or equivalent, - Significant experience building and operating production computer-vision, machine-learning, graphics, or data-processing systems. - Deep hands-on experience with scene understanding: semantic, instance, or panoptic segmentation; open-vocabulary detection and segmentation; or vision-language models applied to perception. - Experience taking 2D perception into 3D, such as multi-view label fusion, 3D semantic segmentation, instance tracking across views, or scene graph construction over reconstructed geometry. - Experience delivering reliable computer-vision or 3D systems used by other teams or customers, including the evaluation that proved they worked.. - Practical command of 3D and spatial data, including geometry, camera models, coordinate frames, calibration, and metric scale. - Strong Python and PyTorch skills, with the ability to work in C++ or other performance-oriented environments when needed. - Experience with cloud infrastructure, GPU workloads, distributed processing, or large-scale data pipelines. Nice to Have - Hands-on experience with 3D reconstruction, photogrammetry, structure from motion, Gaussian splatting, neural rendering, or meshing. - Experience with SLAM, visual positioning, localization, or large-scale mapping systems. - Experience with robotics, simulation, or embodied AI applications, in particular how semantic labels are consumed by a policy or a simulator. - Built evaluation or benchmarking infrastructure for perception or reconstruction systems. - Experience optimizing large-scale GPU inference workloads for cost, throughput, or latency. - A publication record or contributions to widely used open-source perception codebases. - Advanced degree in computer vision, robotics, machine learning, or a related field. Competencies - Relentless bias for action. You make decisions and ship as if the company’s success depends on it, because it does. You set the pace, and drive the people around you to rise to it. - Intellectually honest. You know there’s just one job that underpins every other in a startup: find the truth. You are relentless in your pursuit of it, including when it challenges your own convictions, and you raise concerns when you see them — even when it's uncomfortable, and especially when it's unpopular. - Pragmatic. You have no patience for not-invented-here Syndrome and analysis paralysis. You find the fastest path to a working capability without one-way doors that undermine scaling. - Strong systems instincts. You understand that production quality includes reliability, observability, cost, latency, maintainability, and usability—not just algorithmic accuracy. - Comfort with ambiguity. You can make progress when the problem, data, and requirements are still evolving. Compensation & Benefits Base salary range of $229,500 to $255,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: New York, NY (HQ); Remote (US) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT THE ROLE We’re seeking someone to lead the future of fraud machine learning at Ramp. In this role, you will help build core machine learning models, design data architectures, and set strategic roadmaps to help Ramp mitigate fraud-related threats while minimizing the friction experience by legitimate users. You will partner closely with product and engineering counterparts across model design, implementation, execution, and analysis. WHAT YOU’LL DO - Employ statistical and machine learning techniques on large datasets to discover patterns of fraud, platform abuse, and identity theft - Prototype and productionize machine learning models and rules-based systems to protect Ramp and its users from fraud - Partner closely with Fraud Engineering and Data Platform teams to augment and leverage data across first and third party sources, ensuring we’ve added as much context as possible to every decision we make - Contribute to the culture of Ramp’s machine learning team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way WHAT YOU NEED - Bachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields - A minimum of 5 years of industry experience as a Machine Learning Engineer, Applied Scientist or Data Scientist - Strong python experience (numpy, pandas, sklearn, pytorch etc.) across ML techniques and backend engineering - Prior experience deploying Machine Learning models to production and making meaningful contribution to backend systems - Strong knowledge of SQL (Snowflake, Postgres, etc.) - Fluency with agentic (AI) tools for software development and data analysis - Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions NICE-TO-HAVES - PhD in Math, Economics, Physics, Computer Science, or other quantitative fields - Context on Fraud and/or Identity Threat detection systems - Experience at a high-growth startup - Experience with the modern data stack ( Snowflake / Hex / dbt / RisingWave / etc ) - Strong perspective on data science + ML engineering development cycle, especially in a post-AI setting - Experience developing LLM-backed systems or tools BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: London Workplace: Hybrid About Niantic Spatial We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Role Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. Niantic Spatial's R&D Team in London is seeking a Research Scientist to push the frontiers of Spatial AI, advancing our Large Geospatial Model as a dedicated member of the Embodied AI group. You will invent and develop algorithms and systems that sit at the cross-section of computer vision, machine learning, AI, graphics, and robotics. We're passionate about discovery, so we're looking for scientists who want to learn, publish, teach, and deploy every new invention. What You'll Do - Develop New Spatial Representations - Develop new spatial representations that embodied AI can learn from, train in, and act on. - Advance Real-to-Sim Pipelines - Advance real-to-sim pipelines connecting real-world observations, 3D reconstruction, simulation, and robotic policy learning. - Solve Emerging Spatial Problems - Solve emerging problems that relate to spatial intelligence, 3D scene representation, training data, model efficiency, and interactive systems. - Take Capabilities Through to Evidence - Take new capabilities through to evidence: what changed for the policy, not only what changed in the reconstruction. - Build Prototypes - Build proof of concept prototypes, initially for internal use, where some will need to run at production scale on customer data. - Collaborate Across Research and Product - Collaborate closely with researchers, engineers, and product partners to bring novel models to production. - Publish Your Work - Publish and present your work. What You'll Bring - A PhD in computer vision, robotics, graphics, machine learning, or a related field, or equivalent research experience, with a publication record at top venues. - Substantial expertise in robotics or embodied AI, including experience in sim-to-real transfer, domain randomization, policy evaluation, and simulation-based learning. - Deep experience with 3D scene representations: Gaussian splatting, neural fields, meshes, point clouds, or implicit representations. - You have applied generative or diffusion models to 3D data, such as scene completion, inpainting, relighting, or novel view synthesis from sparse capture. - You have worked on 3D semantics, open-vocabulary scene understanding, or scene graphs. - Proficiency in Python and Deep Learning libraries such as PyTorch. - GPU or graphics programming experience, such as custom CUDA kernels, shaders, or differentiable rendering. - A track record of contributing to widely used open-source research codebases. Nice to Have - Proficiency in C++. Compensation & Benefits Base salary range of £103,500 to £202,500. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. Location & Work Model This role is based in our London office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: London Who are we? Tripledot Studios is one of the largest independent mobile games companies in the world. We are a multi-award-winning organisation, with a global 2,500+ strong team across 12 studios. Our expanded portfolio includes some of the biggest titles in mobile gaming, collectively reaching top chart positions around the world and engaging over 25 million daily active users. Tripledot’s guiding principle is that when people love what they do, what they do will be loved by others. We’re building a company we’re proud of. One filled with driven, incredibly smart and detail-orientated people, who LOVE making games. Our ambition is to be the most successful games company in the world, and we’re just getting started. Role Overview Join Tripledot Studios as a Senior Machine Learning Engineer working on dynamic pricing and recommender systems use cases in our games. The work involves tabular machine learning, including neural networks and approaches such as gradient-boosted decision trees. You'll focus on understanding the data, developing features and improving the models, rather than primarily owning production deployment. You'll work toward defined near-term ML targetds, collaborating with the monetization team and game product teams to plan A/B tests. Over the longer term, you'll work with those teams to translate product and monetization requirements into ML objectives. You'll start by learning the project and its business goals. Within the first three months, we'd expect you to contribute to the training pipeline and investigate features and model issues. By around six months, the aim is for you to take a more proactive role in setting the model's direction. Key Responsibilities Build and improve training pipelines for dynamic pricing and recommender system models, from feature and label design through training, tuning and offline evaluation of tabular models such as neural networks and gradient-boosted trees. Monitor model performance once models are live across games and products. Investigate data and concept drift, including shifts tied to new titles, client versions or user behaviour, and make models easier to extend to another product. Diagnose missed model outcomes across the training pipeline, business logic and underlying data, then work through the improvements needed to restore model quality. Work with monetization and product colleagues to connect model decisions to revenue and player outcomes, and help plan the A/B tests that inform what ships. As you get up to speed, identify gaps in the team's understanding of the models and propose improvements to their direction. Required Skills, Knowledge and Expertise Hands-on training and evaluation of tabular models, using neural networks or gradient-boosted trees. Experience with PyTorch, PyTorch Lightning, TensorFlow, XGBoost, CatBoost or scikit-learn could all be relevant; no single framework is required. Experience designing features and labels, choosing metrics for the decision a model makes and judging when offline results warrant an A/B test. Proficiency in SQL and the ability to write efficient queries to extract, manipulate and aggregate data from relational databases. An investigative approach to incomplete data and longer-term requirements that need clarification. The ability to explain model results to monetization and product partners in terms of revenue and player outcomes. Experience in ad tech, recommender systems or online marketplaces would be useful, as would experience with production APIs or deploying ML models. Experience with the Ray framework would also be a plus. None of these is required for the role. Uses AI-assisted development tools, including code assistants and LLM-based copilots, to accelerate implementation, debugging and iteration of machine learning systems while maintaining production-quality standards. Critically reviews and validates AI-generated code, model implementations and infrastructure configurations for reliability, correctness and maintainability, and explores AI-powered approaches to improve developer productivity or ML platform capability. Working for Tripledot 25 days paid holiday in addition to bank holidays to relax and refresh throughout the year Hybrid Working 20 days remote working: Work from anywhere in the world, or use the time to cover mandatory office days to WFH, 20 days of the year. Daily Free Lunch: when in the office you get £12 every day to order from JustEat. Regular company events and rewards: quarterly on-site and off-site events that celebrate cultural events, our achievements and our team spirit. Employee Assistance Program: Anytime you need it, tap into confidential, caring support with our Employee Assistance Program, always here to lend an ear and a helping hand. Family Forming Support: Receive vital support on your family forming/ fertility journey with our support program [subject to policy] Life Assurance & Group Income Cover: Financial protection for you and your loved ones. Continuous Professional Development: Propel your career with continuous opportunities for professional development. Private Medical Cover & Health Cash Plan: Opt-in (P11d benefit) comprehensive private medical cover with Bupa and cash plan with Medicash. Dental Cover: Opt-in (P11d benefit). Cycle to Work Scheme: Salary sacrifice bike purchase scheme. Pension Plan: Qualifying earnings or opt-in 4% contributory match schemes offered.

Location: Barcelona Who are we? Tripledot Studios is one of the largest independent mobile games companies in the world. We are a multi-award-winning organisation, with a global 2,500+ strong team across 12 studios. Our expanded portfolio includes some of the biggest titles in mobile gaming, collectively reaching top chart positions around the world and engaging over 25 million daily active users. Tripledot’s guiding principle is that when people love what they do, what they do will be loved by others. We’re building a company we’re proud of. One filled with driven, incredibly smart and detail-orientated people, who LOVE making games. Our ambition is to be the most successful games company in the world, and we’re just getting started. Role Overview Join Tripledot Studios as a Senior Machine Learning Engineer working on dynamic pricing and recommender systems use cases in our games. The work involves tabular machine learning, including neural networks and approaches such as gradient-boosted decision trees. You'll focus on understanding the data, developing features and improving the models, rather than primarily owning production deployment. You'll work toward defined near-term ML targetds, collaborating with the monetization team and game product teams to plan A/B tests. Over the longer term, you'll work with those teams to translate product and monetization requirements into ML objectives. You'll start by learning the project and its business goals. Within the first three months, we'd expect you to contribute to the training pipeline and investigate features and model issues. By around six months, the aim is for you to take a more proactive role in setting the model's direction. Key Responsibilities Build and improve training pipelines for dynamic pricing and recommender system models, from feature and label design through training, tuning and offline evaluation of tabular models such as neural networks and gradient-boosted trees. Monitor model performance once models are live across games and products. Investigate data and concept drift, including shifts tied to new titles, client versions or user behaviour, and make models easier to extend to another product. Diagnose missed model outcomes across the training pipeline, business logic and underlying data, then work through the improvements needed to restore model quality. Work with monetization and product colleagues to connect model decisions to revenue and player outcomes, and help plan the A/B tests that inform what ships. As you get up to speed, identify gaps in the team's understanding of the models and propose improvements to their direction. Required Skills, Knowledge and Expertise Hands-on training and evaluation of tabular models, using neural networks or gradient-boosted trees. Experience with PyTorch, PyTorch Lightning, TensorFlow, XGBoost, CatBoost or scikit-learn could all be relevant; no single framework is required. Experience designing features and labels, choosing metrics for the decision a model makes and judging when offline results warrant an A/B test. Proficiency in SQL and the ability to write efficient queries to extract, manipulate and aggregate data from relational databases. An investigative approach to incomplete data and longer-term requirements that need clarification. The ability to explain model results to monetization and product partners in terms of revenue and player outcomes. Experience in ad tech, recommender systems or online marketplaces would be useful, as would experience with production APIs or deploying ML models. Experience with the Ray framework would also be a plus. None of these is required for the role. Uses AI-assisted development tools, including code assistants and LLM-based copilots, to accelerate implementation, debugging and iteration of machine learning systems while maintaining production-quality standards. Critically reviews and validates AI-generated code, model implementations and infrastructure configurations for reliability, correctness and maintainability, and explores AI-powered approaches to improve developer productivity or ML platform capability. Working for Tripledot 25 days paid holiday in addition to bank holidays to relax and refresh throughout the year Hybrid Working 20 days remote working: Work from anywhere in the world, or use the time to cover mandatory office days to WFH, 20 days of the year. Regular company events and rewards: Join in regular events and rewards that celebrate cultural events, our achievements and our team spirit. Private Medical Cover: Have peace of mind with private medical cover, ensuring your health is in good hands. Life & Critical Illness Cover: Protect your future with our life and critical illness cover. Family Forming Support: Receive vital support on your family forming/ fertility journey with our support program [subject to policy] Employee Assistance Program: Access confidential support anytime through our Employee Assistance Program. Sport Compensation: Stay fit and active with our sport compensation benefit. Meal and Transport Vouchers: Save on meals and transport with our convenient vouchers. English & Spanish Classes: Enhance your English and Spanish skills with our provided language classes. Continuous Professional Development: Propel your career with continuous opportunities for professional development.

Location: London, United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Задачи: — Разработка и внедрение ML/NLP решений для реальных продуктовых задач. — Работа с LLM: fine-tuning, prompt engineering, повышение качества ответов. Требования: — Опыт работы Data Scientist от 2 лет. — Уверенный Python. — Практический опыт с ML/NLP стеком (PyTorch, TensorFlow, Hugging Face).

Location: Kyiv, Ukraine; Global Workplace: Remote [https://app.ashbyhq.com/api/images/user-content/eaa93aea-4a8e-480b-ac45-957f53a8b99b/230615d5-1e71-4618-8216-a6082a6261ae/Holy%20earth.png] HOLYWATER TECH https://holywater.tech/ — це технологічна компанія, яка створює розважальні продукти на перетині людської творчості та штучного інтелекту. Вони є прикладами ШІ-нативних платформ, що переосмислюють способи сторітелінгу та дають змогу будь-кому стати творцем. Екосистема продуктів HOLYWATER TECH включає: → My Drama https://apps.apple.com/us/app/my-drama-watch-shorts-movies/id6498713494 — стримінгову платформу вертикальних серіалів, №1 серед європейських та американських компаній у ніші → My Passion https://apps.apple.com/us/app/my-passion-romance-books/id1568149521 — цифрову бібліотеку книг №1 у понад 10 країнах світу → My Muse https://apps.apple.com/ua/app/my-muse-audiobooks-stories/id6738098034 — платформу ШІ-генерованих серіалів, що відкриває нову еру сторітелінгу ___________________________________________________________________________ HOLYWATER TECH СЬОГОДНІ — ЦЕ: - понад 100+ млн завантажень застосунків - стратегічне партнерство з FOX Entertainment - х2 рази — щорічне зростання компанії за 2024-2025 роки - 30+ міжнародних нагород, зокрема Muse Creative, W3, Telly, Runway AI Film Festival, Lovie - понад 100 млн годин переглянутого та прочитаного користувачами контенту - провідні позиції у своїх нішах на ринках США та Європи ___________________________________________________________________________ Ми створюємо нові формати, історії та будуємо світ, де єдине обмеження — людська уява. І зараз шукаємо AI Enginner, який готовий/-а долучитися й творити його разом із нами. ТВОЯ МІСІЯ: - Побудова агентських пайплайнів, що перетворюють ідею на готовий епізод серіалу без ручних етапів між сценарієм, генерацією та монтажем. - Переведення робочих прототипів (MVP, PoC) у продакшн-реді системи з веб-інтерфейсом, якими користується вся команда. - Проєктування єдиної Knowledge Base, доступної одночасно людям і агентам, як основи для спільної роботи різних департаментів. - Швидка інтеграція нових генеративних моделей (відео, текст) у продакшн одразу в день їх релізу. - Пошук і впровадження нових архітектур агентських систем: multi-agent workflows, tool calling, memory, guardrails, self-recovery. - Аналіз патернів у зверненнях користувачів до агентів та автоматизація типових сценаріїв без участі людини. - Скорочення часу і ручної редактури на кожному етапі виробництва контенту — від сценарію до фінального монтажу. - Постійний моніторинг нових моделей, технік і практик у генеративному AI та їх швидка адаптація в продакшн. ___________________________________________________________________________ ДЛЯ ЦЬОГО ЗНАДОБЛЯТЬСЯ ТАКІ НАВИЧКИ ТА ДОСВІД: - 3-4+ років на позиції engineer/developer. - Досвід роботи з Typescipt, Nest.js та Next.js. - Досвід з PostgreSQL, Redis, Docker. - Розуміння Observability. - Продакшен досвід з Agentic AI Architecture: проєктування автономних агентів, ієрархічного планування, оркестрації multi-agent систем, динамічних воркфлоу, tool execution та персистентного стану з використанням TypeScript і LangGraph.js/Mastra. - Досвід з AI Memory & Knowledge Architecture: GraphRAG, векторні бази даних, Neo4j, графи знань, entity resolution, семантичний пошук та довготривала пам'ять агентів. - Досвід з мультимодальним генеративним AI: оркестрація LLM, vision-language моделей та моделей генерації відео; автоматизована оцінка якості, візуальна консистентність, ітеративна регенерація та human-in-the-loop архітектури. - Досвід впровадження Agentic AI systems — multi-agent workflows, tool calling, MCP, RAG, state/memory, structured outputs, evaluations, guardrails і recovery. - Досвід роботи з генеративним відео — video generation models/API, асинхронні медіапайплайни, FFmpeg, забезпечення consistency та автоматичний quality control. БУДЕ ПЕРЕВАГОЮ: - Python для AI/ML-прототипів і роботи з моделями - Fine-tuning, LoRA, PyTorch - GPU infrastructure та inference optimization - Computer vision, lip-sync, character consistency - Knowledge graphs і hybrid RAG ___________________________________________________________________________ ЩО ОТРИМАЄШ ВІД НАС: - Швидке професійне зростання. Ми будуємо середовище, де розвиток — норма: лекції, бібліотеки, хакатони, школи. Компенсуємо профільне навчання й вивчення будь-якої мови, зокрема української. - Формат роботи на твій вибір. Можеш працювати вдома, в офісі на київському Подолі чи у Львові (генератори, укриття, обіди, снеки — all inclusive), або ж за кордоном — маємо кілька офісів і там. - Техніку та сервіси. Все, що треба для роботи. - Відпочинок. 20 днів відпустки + на офіційні свята та безлімітні лікарняні. - Турботу про тіло та менталку. Компенсуємо спорт, медстрахування в Україні (після випробувального), послуги корпоративного лікаря і підтримку ментального здоров’я& ___________________________________________________________________________ ЧОМУ З НАМИ КЛАСНО? - Реальний вплив. Твої рішення впливають на 100+ млн користувачів — їхній досвід, емоції та час, який вони проводять у продукті. - Жива культура. Ми створюємо середовище, де можна експериментувати й пробувати щось нове. Середня оцінка відповіді на питання «Наскільки тобі подобається працювати в HOLYWATER?» за результатами eNPS — 9,23/10. - Автономність. Ми довіряємо рішенням команди: мінімум мікроменеджменту, максимум простору для ініціативи й впливу. - Сильні люди поруч. Ми постійно челенджимо статус-кво та досягаємо нових вершин. Думати інакше й прагнути більшого — тут це підтримають. Можливо, це — твій шанс стати нашим наступним big name? ___________________________________________________________________________ ХОЧЕШ БІЛЬШЕ ПРО HOLYWATER TECH? ЧЕКНИ ЦЕ: Як працює HOLYWATER TECH — на Vector Media https://vctr.media/ua/vid-banochki-svyatoyi-vodi-zi-lvova-do-ugodi-z-fox-entertainment-yak-praczyuye-holywater-startap-yakij-buduye-netflix-dlya-vertikalnogo-video-318305/ Інтерв’ю со-CEO Богдана Несвіта https://www.linkedin.com/in/bogdannesvit/ про партнерство FOX Entertainment для Forbes https://forbes.ua/innovations/maemo-60-mln-koristuvachiv-i-rostemo-duzhe-shvidko-ukrainska-holywater-znimatime-vertikalni-seriali-z-amerikanskoyu-fox-yak-vona-zaluchila-takogo-partnera-intervyu-25102025-33594 Як ми створюємо вертикальні серіали — у подкасті на DOU https://youtu.be/E5nKjr605vs?si=eHKKN6MhQrbEzi8k Про вертикальні серіали, які змінюють індустрію — у Детектор медіа https://detector.media/mediumy/article/248303/2026-03-13-vid-tiktok-do-gollivudu-prodyuserka-holywater-tech-pro-vertykalni-serialy-yaki-zminyuyut-industriyu/ Наші Instagram https://www.instagram.com/holywater.tech/ та LinkedIn https://www.linkedin.com/company/holywater/?viewAsMember=true — щоб бути в курсі всього, що в нас відбувається Хочеш творити історію разом із нами в HOLYWATER TECH? Відгукнися на вакансію!

Задачи: — Разработка и внедрение ML/NLP решений для реальных продуктовых задач. — Работа с LLM: fine-tuning, prompt engineering, повышение качества ответов. Требования: — Опыт работы Data Scientist от 2 лет. — Уверенный Python. — Практический опыт с ML/NLP стеком (PyTorch, TensorFlow, Hugging Face).

Обязанности: • Разработка и внедрение ML/NLP решений для реальных продуктовых задач. • Работа с LLM: fine-tuning, prompt engineering, повышение качества ответов. Требования: • Опыт работы Data Scientist от 2 лет. • Уверенный Python. • Практический опыт с ML/NLP стеком (PyTorch, TensorFlow, Hugging Face).

Основные требования: — Опыт разработки и применения моделей машинного обучения для решения прикладных задач: прогнозирования, классификации, оптимизации, выявления аномалий и анализа производственных процессов. — Понимание основных этапов разработки ML-решений: сбор и подготовка данных, исследовательский анализ, обучение моделей, оценка качества, тестирование и внедрение результатов. — Уверенные навыки программирования на Python и опыт работы с основными библиотеками анализа данных и машинного обучения: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch или аналогичными инструментами. — Опыт работы с временными рядами, историческими данными и построением прогнозных моделей. — Навыки обработки, очистки, объединения и анализа больших объемов производственных и технологических данных. — Общее понимание процессов энергетической отрасли: производство, передача, распределение и потребление энергетических ресурсов, режимы работы оборудования, технологические показатели и показатели эффективности. — Навыки работы с SQL, базами данных и инструментами хранения и обработки информации. — Умение формировать качественные наборы данных для аналитики и обучения моделей машинного обучения. Чем предстоит заниматься: — Разрабатывать и внедрять алгоритмы машинного обучения и интеллектуального анализа данных для задач энергетической отрасли. — Создавать модели прогнозирования производственных и технологических показателей энергетических объектов. — Разрабатывать прогнозы производства, потребления, нагрузки и других ключевых параметров на различных временных горизонтах. — Анализировать технологические и эксплуатационные данные, выявлять зависимости, закономерности, отклонения и потенциальные точки повышения эффективности. — Разрабатывать модели для оценки и прогнозирования состояния оборудования и технологических процессов. — Решать задачи оптимизации режимов работы энергетического оборудования и объектов. — Создавать алгоритмы выявления аномалий, отклонений и нестандартных режимов работы. Что мы предлагаем: — Участие в разработке современных цифровых и интеллектуальных решений для энергетической отрасли. — Работа над практическими задачами цифровизации, аналитики и повышения эффективности энергетических объектов. — Возможность применять технологии Machine Learning, Data Science и Artificial Intelligence для решения реальных производственных задач. — Работа с промышленными, технологическими и эксплуатационными данными энергетических предприятий. — Участие в проектах по прогнозированию, оптимизации, предиктивной аналитике, мониторингу оборудования и развитию цифровых двойников. — Возможность профессионального развития в направлениях Data Science, Machine Learning, Industrial AI и цифровой энергетики. — Взаимодействие с командой специалистов в области IT, анализа данных, автоматизации и энергетики. — Возможность участвовать в проектах, направленных на повышение надежности, эффективности и технологичности энергетической инфраструктуры. — Конкурентные условия труда и возможности для долгосрочного профессионального развития.