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Location: Palo Alto, California, United States 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 Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering. A Senior Machine Learning Engineer owns substantial ML work end to end. They can translate an ambiguous capability goal into concrete experiments, implement and debug training and RL recipes, build the supporting data and systems, and deliver measurable improvements in model quality, experiment throughput, and reliability. They are deeply hands-on and can independently debug both model-behavior failures and distributed training failures. Your responsibilities : Design and run model-training and post-training experiments, including SFT , continued pretraining, preference optimization ( DPO /IPO/ KTO ), and RL methods such as RLHF / RLAIF , PPO , and GRPO . Build reward functions, judge models, verifiers, task environments, and evaluation sets for reasoning, coding, tool use, and agentic workflows. Create synthetic data and data pipelines, including teacher-student generation, self-play, rejection sampling, filtering, and quality scoring. Analyze model-behavior failures and turn them into targeted data, reward, or algorithm improvements. Build and maintain distributed training and RL infrastructure using frameworks such as Megatron- LM , DeepSpeed, PyTorch FSDP /DTensor, Ray, verl, slime, AReaL, or OpenRLHF. Implement and debug parallelism strategies (tensor, pipeline, sequence/context, expert, and data parallelism) and build reliable rollout, reward-serving, checkpointing, and experiment-orchestration components. Profile and improve GPU utilization, memory usage, communication efficiency, training throughput, and inference/serving performance. Design rigorous evaluations and ablations for capability, instruction following, reasoning, tool use, safety, and regression risk. Write clear experiment plans, design docs, benchmark reports, and runbooks, and partner across research and platform teams. Must-haves : Strong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system. Hands-on experience across at least two of: model training, post-training/ RL , applied modeling, data pipelines, or large-scale ML systems. Ability to design rigorous experiments with baselines, ablations, metrics, and failure analysis. Practical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges. Practical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing. Ability to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity. Strong communication skills and ability to collaborate with researchers, engineers, and leadership. Nice - to - have s : Experience with LLM post-training, RL , agents, reward modeling, synthetic data, or model evaluation. Experience with RL frameworks or pipelines such as verl, slime, AReaL, OpenRLHF, TRL , or custom PPO / GRPO / RLHF systems. Experience with Megatron- LM , DeepSpeed, PyTorch FSDP /DTensor, Ray, Slurm, or Kubernetes on large GPU clusters. Familiarity with NCCL , CUDA , Triton, Nsight, InfiniBand/ RDMA , and H100/H200/B200 clusters, or with model serving and inference optimization. Publications, open-source contributions, or production impact in LLM post-training, RL , reasoning, coding models, synthetic data, distributed training, or evaluation. Experience designing agent environments, tool-use tasks, or verifier-based rewards. Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $195,200 — $262,200 USD 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 | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change. Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it. Key responsibilities Build and scale the infrastructure and data pipelines behind Safeguards machine learning research Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve Take the highest-value research workflows from experiments to reliable, production-grade jobs Improve the throughput, cost, and reliability of large-scale inference and scoring workloads Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time Minimum qualifications Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python Experience building and operating data-intensive or distributed systems in production Experience building tooling or infrastructure that other engineers or researchers use as a dependency Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems Ability to debug performance and correctness problems across an unfamiliar stack Strong written and verbal communication skills, and a collaborative approach to technical decisions Preferred qualifications Experience with high-performance, large-scale machine learning systems Familiarity with language modeling and transformers, including working with model internals Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization Experience building experiment tracking, caching layers, or evaluation harnesses for research teams Experience with probes, interpretability, or classifier development Interest in the misuse risks of AI systems and a desire to work on mitigating them The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $500,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. By providing members with a deep understanding of their bodies, behaviors, and daily lives, WHOOP empowers healthier choices and peak performance. We are seeking a Principal AI Researcher to join our Foundation AI team. This team builds the foundation models that underpin WHOOP’s next generation of intelligent, personalized, and health-enhancing experiences. These models integrate data across wearable sensors, language, biomarkers, clinical information, and self-reported inputs to create scalable AI systems that understand human physiology and behavior. In this role, you'll be one of the senior technical leaders at WHOOP.  You will own the technical execution of our foundation modeling workstreams and your technical judgment will shape a core part of WHOOP’s AI efforts and the products they power. RESPONSIBILITIES: - Set the architectural direction for the development of large-scale models spanning wearable sensor data, text, biomarkers, and behavioral signals, and personally own the highest-stakes, hardest-to-reverse design decisions. - Identify which research bets in self-supervised and representation learning are worth making, and drive the most ambiguous and unproven ones from thesis to validated, production capability. - Contribute to the broader AI  strategy and standards at WHOOP, shaping the platform, compute, and infrastructure choices the team operates on, not just working within them. - Partner across research, product, and engineering to connect technical breakthroughs to product and business outcomes. - Raise the technical bar across the team: grow staff and senior engineers, strengthen design-review and evaluation norms, and act as a force multiplier. - Represent WHOOP's technical work externally, helping with recruiting, partnerships, and visibility in the broader AI research community. QUALIFICATIONS: - Advanced degree (Master’s or Ph.D.) or equivalent depth, with extensive experience in large-scale machine learning and AI research. - A track record of owning technical direction for a significant area and making architectural decisions whose impact outlived any single project. - Demonstrated ability to take ambiguous, high-risk research directions all the way to production impact and the judgment to discontinue those that will not. - Deep expertise in modern deep learning (transformers, state space models), multimodal training, self-supervised and representation learning, RL-based post-training (PPO/DPO/GRPO), and large-scale distributed training (data, model, and context parallelism). - A history of being a technical authority that teams defer to, and evidence of multiplying their output. - Excellent communication and the ability to influence both the engineering bench and senior leadership. - Publications and evidence of community building at top-tier machine learning venues.  - Passion for WHOOP's mission to improve human performance and extend healthspan through science and technology.  This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.  Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $270,000 - $300,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.  In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

Location: Remote Canada At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do - You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. - Identify and implement foundational improvements to how the team builds models. - You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience. - Track record of delivering high impact machine learning models in a low latency live setting - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning framework (PyTorch preferred). - Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar). - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms). - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows. - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code. - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews. - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders. - You have strong verbal and written communication skills that support effective collaboration with our global engineering team. Pay Grade - N Equity Grade - 6 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $153,000 - $213,000 Location - Remote Canada This remote role is open only to candidates residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan. #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and their dependents. Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you. Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to. Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described.

Location: Spain; Serbia; Poland; Ukraine; Portugal; North Macedonia; Bosnia and Herzegovina; Romania Workplace: remote About Provectus Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.   Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.   Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX, Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for . Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire:   Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant  HR Interview. Soft skills and expectations HM interview. Tech questions; a live engine

Задачи: - Разрабатывать бэкенд LLM-агента для автоматизации поддержки - Учиться строить LLM-продукты и участвовать в формировании платформы для их развития - Следить за надежностью и качеством решений — агент будет работать под нагрузкой и решать проблемы миллионов пользователей - Решать задачи на стыке с Data Science, собирать легкие прототипы новых фич для проверки на пользователях - Следить за надежностью и качеством решений — внутренние инструменты работают с теми же гарантиями, что и внешние: мониторинг, алертинг, SLA Требования: - Опыт разработки на Python с использованием FastAPI, Pydantic, LangChain, LangGraph, LangFuse, punq - Опыт разработки распределенных систем: Kafka, PostgreSQL, Redis, Elastic - Опыт разработки сложных распределенных систем - Способность быстро выполнять задачи без критических ошибок - Приветствуется опыт работы с ML-продуктами и интерес к ML, NLP, LLM

Location: Bremen, Germany Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology About the Position The research group of Dr. Sari Sadiya at Constructor Knowledge Labs (CKL) , in collaboration with Constructor University (CU) and Constructor Technology (CT) , invites applicants for Ph.D. student positions in Computer Science with a focus on wearable intelligence, multimodal data fusion, and edge AI . The project investigates how wearable data streams can be transformed into structured knowledge and integrated into a knowledge-grounded digital avatar that supports bi-directional collaboration in education and research. Ph.D. students will work in a highly interdisciplinary environment, combining AI/ML, edge computing, cognitive science, and human–computer interaction . In close collaboration with academia and industry, they will contribute to building privacy-preserving, real-time personalization frameworks while pursuing their doctoral dissertation. About the Program The PhD program is research-centered, emphasizing original contributions in: Wearable data analytics and fusion methods Biomedical data analytics On-device processing and performance modeling Personalization and adaptive reasoning systems AI for Education Doctoral students will also have access to specialized courses in: Artificial Intelligence, Machine Learning, and Edge Computing Advanced Computational Methods and Data Science Cognitive Science and Human-Centered Computing As part of the program, students will collaborate with Constructor Technology to gain first-hand industrial experience, contributing to real-world testbeds and prototypes. Research Focus This PhD position is part of the Wearable Intelligence Project , with two main research directions: Data fusion of heterogeneous temporal streams Designing algorithms to unify multimodal signals (physiological, cognitive, contextual, scheduling, and learning data). Developing pipelines that produce structured insights powering the Agentic Personalization Engine (APE) . On-device data processing & performance modeling Developing models balancing computation, energy, and data flows across wearable, edge, and cloud environments. Exploring feasibility of running compact micro-LLMs directly on wearables . The overarching goal is to create scalable, ethical, and transparent personalization systems that support education and research. Funding The appointment provides full financial coverage through a dedicated fellowship, comprising: Monthly stipend of €1,650 Monthly research-cost allowance of €100 (Forschungskostenpauschale) Health-insurance subsidy of €100 per month Supplementary €603 mini-job allowance to support parallel part-time employment (optional) Constructor Knowledge Labs actively supports candidates in preparing applications for external funding — doctoral scholarships, foundations, or international mobility grants — and can provide institutional support and references Applicant Profile Mandatory requirements: MSc degree (or equivalent) in Computer Science, AI/ML, Data Science, Cognitive Science, or related disciplines. Strong background in AI/ML, signal processing, or edge computing. Hands-on experience with wearable or multimodal data (e.g., heart rate, EEG, activity, sleep, GPS) . Solid mathematical and computational modeling skills. Proficiency in academic English writing (e.g., reports, papers, theses). Preferred qualifications: Experience with LLMs, multimodal data fusion, or agent-based AI systems . Familiarity with privacy-preserving ML, dynamic consent, and GDPR-compliant frameworks . Demonstrated ability to conduct independent research and collaborate across disciplines. Interest in teaching, mentoring, and applied industrial research. Application Details Deadline : August 31, 2026 Required documents: Curriculum Vitae (CV); Academic transcripts; Letter of motivation outlining research interests and career goals; 2 recommendation letters. Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. OUR MISSION At Humanoid we strive to create the world's leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity. ABOUT THE ROLE As a Research Engineer on the World Models team, you will build action-conditioned generative models that predict how the world evolves around our robots — future video, proprioception, contacts, and outcomes — from past observations and actions. World models serve four purposes in our stack: a pretrained, physics-aware prior for our VLA policies; an engine for rare data collection, cross-platform transfer, and sim-to-real transfer; a testbed for policy evaluation and testing before hardware; and a future-prediction rollout engine that surfaces what our policies intend to do, for safety and planning. This is a hands-on individual contributor role: you will design architectures, run large training jobs, and validate your models against real fleet data from industrial deployments. WHAT YOU'LL DO - Design and train multimodal world models — video, state, action, and language — using diffusion-based and transformer architectures. - Build action-conditioned video prediction and dynamics models that stay physically consistent over long horizons, including contact-rich manipulation, and serve as pretrained priors for VLA policies. - Develop learned-simulator evaluation: score candidate policies offline, predict real-world success rates before deployment, and roll out policy futures to expose intended behaviour for safety review and planning. - Generate synthetic rollouts and counterfactual experience — including rare events, cross-platform transfer, and sim-to-real transfer — to augment policy training, and measure their effect on downstream task performance. - Establish fidelity metrics and calibration protocols that quantify where the world model can be trusted and where it diverges from reality. - Build data pipelines that turn fleet telemetry, teleoperation logs, and internet-scale video into training corpora for world models. - Run scaling and ablation studies on architecture, data mixture, and context length; communicate findings crisply. - Collaborate with pretraining, RL, and manipulation teams to integrate world models into policy training and evaluation loops. WHAT WE'RE LOOKING FOR - A track record of training large generative models — video, world, or multimodal — with shipped models or published artifacts to show for it. - Deep hands-on experience with modern generative architectures: diffusion models, autoregressive transformers, latent-variable models, or video prediction. - Experience with large-scale distributed training: streaming datasets, checkpointing and state management, debugging numerics and training instabilities. - Strong Python + PyTorch/JAX; you can profile kernels, optimize data loaders, and write maintainable research code. - Empirical rigor: you design careful evaluations, run honest baselines, and document experiments clearly. - Excitement about grounding generative models in physical reality rather than pixels alone. NICE TO HAVE - Experience with world models for robotics or autonomous driving (e.g., action-conditioned video models, learned simulators, model-based RL). - Familiarity with robotics simulators (Isaac Sim, MuJoCo) and sim-to-real considerations. - Experience using world models for policy evaluation or synthetic data generation at scale. - Publications at top-tier deep learning conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions. - Experience optimizing generative models for fast inference. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Reinforcement Learning Engineer to join our Autonomy team based in London. In this role you will leverage reinforcement learning in both simulation and physical reality to build highly performant and robust manipulation policies. WHAT YOU'LL DO - Train language-vision conditioned manipulation policies via reinforcement learning (RL) in simulation and in the real world. - Construct challenging and diverse suites of manipulation tasks in simulation. - Partner with teleoperations to collect trajectories in simulation for behavior cloning. - Partner with testing and operations to establish real-world RL training pipelines. - Experiment with various ways of bringing policies trained in simulation to the real world. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. - Experience solving real problems using reinforcement learning with deep neural networks in any domain. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade‑offs crisply. NICE TO HAVE - Experience with simulators for robotics (Isaac Sim, MuJoCo etc.) - Experience in RL for robotics. - Experience building infrastructure for large-scale RL (e.g. using ray). - Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions. - Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a VLA Pre-training Engineer to join our Autonomy team based in London. In this role you will you will work on all aspects of training capable policies, be it pre-training of a base model on a diverse multi-embodiment corpus of trajectories, fine-tuning a policy to perform a specific task well, curating data collection processes or exploring productive ways to generate and use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, while experience in robotics isn’t strictly required. However if you don’t have such experience, be prepared that you’d need to familiarize yourself with a new domain quickly.   WHAT YOU'LL DO - Post-train policies via behaviour cloning and RL; own the full loop from data to deployment. - Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage. - Work closely with external partners to ensure steady supply of high-quality pretraining-scale data. - Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes. - Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining. - Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. - Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - Familiarity with modern software engineering practices. - You document experiments clearly and communicate trade‑offs crisply. NICE TO HAVE - Robotics or autonomous driving experience. - Experience applying RL to LLMs or robotics. - Experience with VLA (vision-language-action) models. - Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization). - Publications at top-tier deep learning conferences or equivalent open‑source contributions. - Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: Boston, MA Workplace: OnSite WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives. Our Machine Learning Core Algorithms team is responsible for developing novel algorithms and features that expand our health and fitness capabilities with wearable sensor data. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. This role is located on our Core Algorithms team: performance-related insights for sleep, recovery, and exercise. As a Senior Machine Learning Engineer on our Core Algorithms team, you will design, build, and productionize ML systems that deliver meaningful, personalized metrics to millions of members. You will own the ML systems you build in collaboration with applied machine learning scientists. You will work at the intersection of data science, backend engineering, and cloud infrastructure – deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML systems. Responsibilities: - Create, improve, and maintain production services that provide analysis for core features in collaboration with applied ML scientists and MLOps engineers. - Improve ML data pipelines, tooling, and validation systems that support robust model performance. - Work alongside applied ML scientists to translate research prototypes into production ML systems optimized for scale, latency and cost efficiency. - Collaborate with researchers and Product teams to align model development with physiological insights and member impact. - Build operational maturity within the Core Algorithms team as well as the broader Machine Learning team, developing processes for observability, alerting, and incident response (including on-call rotations). - Develop applied ML operational infrastructure (frameworks, evaluation criteria, performance validation). - Mentor other engineers and applied ML scientists on production practices, raising the bar through code and system design review. Qualifications: - Bachelor’s Degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master’s preferred). - 4+ years of professional experience as an ML engineer, applied researcher, or software engineer with a focus on ML systems. - Experience working with time series data (wearable, physiological, or high-frequency sensor data). - Strong coding skills in Python with a track record of writing clean, production-quality code. - Experience designing, deploying and operating ML production systems at production scale (millions of users, real-time streaming and/or large-scale batch). - Experience deploying and maintaining ML backend services (APIs, reliability, observability, monitoring; AWS or GCP). - Preferred: 2+ years experience applying advanced mathematical and statistical techniques. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.    Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.   WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.   At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.   The U.S. base salary range for this full-time position is $150,000-$210,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.    In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.   These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.   Learn more about WHOOP https://www.whoop.com/us/en/careers/?srsltid=AfmBOopKmph9d0DLBlogZu8mx6do0dzjNS8eJlfc4PQqQtdU6F8DGKBg.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level and live longer through a deeper understanding of their bodies and daily lives. We are seeking an Associate Director, Core Algorithms (Cloud) to lead the teams responsible for WHOOP's cloud-based algorithmic intelligence — the models and systems that transform physiological data into the sleep, recovery, and training insights our members rely on daily. This role is also responsible for ensuring our cloud algorithms evolve alongside WHOOP hardware, partnering with Sensor Intelligence and Hardware teams to translate new sensor capabilities into production-grade algorithmic experiences. You will own the technical vision, execution, and organizational health of this team. You'll drive the evolution of our core production algorithms such as workout detection, strain, and sleep staging toward higher accuracy, better member experiences, and more mature development practices. You will partner closely with ML Platform, Sensor Intelligence, Research, Product, and Software Engineering to define what WHOOP algorithms can enable — not just how they perform technically, but how they show up for members. This is a role for someone who has built and shipped physiological ML at scale in a consumer product, who has a deep product instinct for what algorithms mean to end users, and who knows how to elevate an ML organization's practices: raising the bar on tooling, processes, and standards to match the ambition of the work. RESPONSIBILITIES - Lead the cloud ML team responsible for the algorithms powering sleep, recovery, and training - Directly manage applied ML scientists and ML engineers; provide coaching, career development, and performance feedback that grows individual contributors into strong technical leaders - Ensure the technical quality bar for algorithm development is maintained by establishing the processes, reviews, and standards that guarantee rigor from research through deployment, and diving into designs and architectural decisions where necessary - Help drive the vision for what WHOOP algorithms and next-generation sensors can enable; advocate for member experience and push the boundaries of what our data makes possible - Ensure cloud algorithms remain compatible with future hardware generations; partner with Sensor Intelligence and Hardware to evolve proof-of-concept algorithms that leverage new sensor capabilities and bring them to production readiness - Establish and improve development lifecycle practices: experiment management, model validation, deployment pipelines, and production monitoring - Partner with ML Platform / MLOps to define requirements and drive maturity improvements across experiment tracking, model monitoring, deployment automation, and observability - Drive cross-functional alignment with Sensor Intelligence, Product, Software Engineering, and Research teams QUALIFICATIONS - 8+ years of experience in machine learning or applied data science, with hands-on experience developing and shipping ML models for a consumer product - 4+ years of people leadership experience directly managing machine learning scientists/engineers, with demonstrated growth of team members and a track record of building high-performing teams - Experience scaling a production ML organization: growing teams and leaders, identifying gaps in the development lifecycle, and driving improvements that increase velocity, reliability, and rigor - Deep product sense: ability to think about algorithms from the member's perspective, drive the vision for what algorithms can enable, and ensure the team is building toward meaningful user outcomes - Ability to evaluate technical designs, guide architectural decisions, and ensure quality at the system level, without needing to write code day-to-day - Experience defining and driving cross-functional programs with engineering, product, and science partners - Strong communication skills with the ability to translate complex ML concepts to diverse audiences including product, engineering, and executive stakeholders PREFERRED - Experience building algorithms using physiological or wearable sensor data (e.g., PPG, accelerometer, temperature, bioimpedance) - Experience managing through hardware-coupled development timelines where sensor availability and device generations constrain algorithm roadmaps - Familiarity with time-series modeling, sequential data, and the specific challenges of continuous physiological monitoring This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.    Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.   WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.   At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.   The U.S. base salary range for this full-time position is $200,000-$245,000 Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.    In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.   These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.   Learn more about WHOOP https://www.whoop.com/us/en/careers/?srsltid=AfmBOopKmph9d0DLBlogZu8mx6do0dzjNS8eJlfc4PQqQtdU6F8DGKBg.

Location: Sahibzada Ajit Singh Nagar (Mohali), India Workplace: hybrid We are currently seeking an Applied Machine Learning / Computer Vision Engineer to join one of our clients’ innovative AI teams. This is an exciting opportunity to work on a next-generation AI platform combining Computer Vision, Multimodal AI, Applied Machine Learning, and Large Language Models . Key Responsibilities As an Applied ML / Computer Vision Engineer, you will: Design, develop, and improve Computer Vision and image-processing pipelines. Build solutions for video analysis, video understanding, and real-time visual processing. Develop multimodal AI systems combining vision, audio, and language data. Work with object detection, image segmentation, classification, tracking, and related Computer Vision techniques. Implement and fine-tune Vision-Language Models and Transformer-based architectures. Integrate Large Language Models into AI products for reasoning, analysis, and automated decision-making. Evaluate, optimize, and deploy machine-learning models in production environments. Improve model inference speed, accuracy, scalability, and resource efficiency. Translate research papers and emerging AI techniques into practical product features. Build APIs and production-grade services for AI model integration. Collaborate with engineering and product teams to understand business requirements and deliver effective AI solutions. Monitor model performance and continuously improve deployed systems. Maintain clear, reusable, and well-documented code. 1–4 years of experienc e in Applied Machine Learning, Computer Vision, Deep Learning, or a related field. Strong programming skills in Python. Hands-on experience with PyTorch. Practical knowledge of OpenCV and image or video processing. Strong understanding of Deep Learning fundamentals. Experience with object detection and/or image segmentation models. Knowledge of Transformer architectures. Experience working with Vision-Language Models. Familiarity with the Hugging Face ecosystem. Experience with model inference, evaluation, and optimization. Understanding of how to move models from experimentation into production. Experience using Docker and Git . Ability to independently investigate technical challenges and propose practical solutions. Ability to understand, reproduce, and implement methods described in research papers. Strong analytical and problem-solving skills. Curiosity and willingness to explore new technologies. Passion for AI, Machine Learning, and product development. Ownership mindset and accountability for delivered solutions. Ability to work independently and contribute within a collaborative team. Interest in building practical products rather than focusing only on model experimentation. Comfortable working in a fast-moving and research-driven environment. Nice to Have Experience with one or more of the following technologies would be considered an advantage: YOLO GroundingDINO SAM or SAM2 Florence-2 Whisper Qwen2.5-VL LLaVA InternVL CUDA TensorRT ONNX FastAPI (Exceptional fresh graduates may also be considered if they can demonstrate strong technical knowledge through relevant academic work, personal projects, GitHub repositories, Kaggle participation, research, or a technical portfolio.)

About the role We’re building a high-performance computer vision system that detects and recognizes objects on fast-moving trains and trucks. The application runs on Linux and is built with Rust/C++ and OpenCV. You’ll join a small team of engineers and ML specialists, working directly with the technical lead on software that powers logistics, automation, tracking, and access control for ports, railways, and industrial facilities. This is not an ML research role. Our ML team trains the models—you’ll focus on everything around them: recognition quality, algorithms, tooling, performance, and developer experience. What you’ll do Improve recognition quality through data analysis and application logic Build internal tools, scripts, and developer utilities Refactor and optimize existing systems for performance and stability Contribute to other engineering projects when needed (web, mobile, embedded, infrastructure) Depending on the project, you may also work with Go, Python, React/TypeScript, SQL/Postgres, and Linux tooling. We’re looking for someone with Strong Rust, C++, or equivalent systems programming skills Solid Linux, Git, and command-line experience Basic understanding of ML or data analysis Good written and spoken English We value engineers who are curious, take ownership, debug systematically, communicate well, and enjoy solving challenging real-world problems. Nice to have OpenCV, CUDA, deep learning, or custom ML model training Linux internals, performance optimization, or low-level debugging Docker, networking, distributed systems, or embedded development Strong personal or open-source projects Why Bravora? We’re a small, fully remote engineering team where you’ll have real ownership, direct impact, and plenty of room to grow. We actively modernize our stack, encourage AI-assisted development, and provide access to state-of-the-art LLM tools. Interested? Send us your CV, GitHub or portfolio (if available), your earliest start date, expected hourly rate, and a few sentences about why this role interests you.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. By providing members with a deep understanding of their bodies, behaviors, and daily lives, WHOOP empowers healthier choices and peak performance.  We are seeking a Senior AI Researcher to join our Foundation AI team. This team builds the multimodal foundation models that underpin WHOOP’s next generation of intelligent, personalized, and health-enhancing experiences. These models integrate data across wearable sensors, language, biomarkers, clinical information, and self-reported inputs to create scalable AI systems that understand human physiology and behavior. In this role, you’ll serve as a senior individual contributor driving the research, development, and deployment of large-scale multimodal models. You’ll collaborate closely with data scientists, ML engineers, and cross-functional partners to push the boundaries of deep learning and ensure our models deliver measurable value to WHOOP members. RESPONSIBILITIES: - Design, train, and optimize large-scale multimodal foundation models that integrate wearable sensor data, text, biomarkers, and behavioral data. - Conduct applied research in self-supervised learning, representation learning, and downstream task fine tuning to advance WHOOP’s core model capabilities. - Develop scalable, distributed training pipelines for large models on high-performance compute environments. - Collaborate with MLOps, data engineering, and software engineering teams to operationalize models for production deployment, ensuring robustness, reproducibility, and observability. - Partner with product and research teams to translate foundation model capabilities into downstream features that deliver meaningful member value. - Contribute to the technical roadmap and architectural direction for foundation model development at WHOOP. - Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI. QUALIFICATIONS: - Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent professional experience. - 7+ years of experience in applied ML, AI research, or large-scale modeling, with a track record of delivering production systems. - Expertise in modern deep learning (e.g., transformers, state space models), multimodal model training.  - Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).  - Familiarity with training models on  mulit-node, multi-gpu distributed compute environments.  - Familiarity with best practices for data, model, and context parallelisms. - Strong applied experience with representation learning, self-supervised methods, and post-training for downstream applications. - Experience with reinforcement learning for post-training foundation models (PPO, DPO, GRPO etc.).  - Familiarity with MLOps best practices including model versioning, evaluation, CI/CD for ML, and cloud-based compute. - Excellent communication skills and ability to collaborate cross-functionally with engineers, researchers, and product teams. - Passion for WHOOP’s mission to improve human performance and extend healthspan through science and technology.   This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.    Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.   WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.   The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.   At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.   The U.S. base salary range for this full-time position is $190,000 - $230,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.    In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.   These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

Location: Seoul Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. About The Job   Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle.   What you will do   • Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk. • Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations. • Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. • Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines. • Manage research projects and communications with client research teams.   About you   • You are fluent in English and Korean, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences. • You’re an expert with PyTorch or JAX. • You’re not afraid of contributing to a big codebase and can find yourself around independently with little guidance. • You write clean, readable, high-performance, fault-tolerant Python code. • You don’t need roadmaps: you just do. You don’t need a manager: you just ship. • Low-ego, collaborative and eager to learn. • You have a track record of success through personal projects, professional projects or in academia.   It would be great if you    • Hold a PhD / master in a relevant field (e.g., Mathematics, Physics, Machine Learning), but if you’re an exceptional candidate from a different background, you should apply. • Can bring a variety of research experience (agents, multi-modality, robotics, diffusion, time-series). • Have contributed to a large codebase used by many (open source or in the industry). • Have a track record of publications in top academic journals or conferences. • Love improving existing code by fixing typing issues, adding tests and improving CI pipelines.   WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Los Angeles, USA At Riot, we are investing in League of Legends to grow the game for generations to come. As player needs become more varied and our experiences become more dynamic, machine learning is an increasingly important part of how we help players discover the right experiences, make better decisions in and around the game, and find fair, compelling matches. As a Staff Machine Learning Engineer in League of Legends, you will build applied machine learning systems that directly improve player experience. You will work across player and product problems through data, modeling, experimentation, launch, and iteration, partnering closely with League product, design, Insights, game engineering, and service engineering teams. Your work could span across personalized player experiences, in-game systems, and matchmaking. You will report to the Data Engineering Director in League of Legends and operate as a deeply embedded technical partner to the game team. You will also help strengthen craft standards, knowledge sharing, and technical quality across Riot’s growing ML engineering discipline. Responsibilities: Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking, from problem framing through production launch and ongoing iteration. Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns that raise the bar beyond your immediate team. Build models, recommenders, ranking systems, and decision logic that help players discover the right champions, builds, modes, content, and return paths based on their needs and context. Develop ML approaches that improve in-game systems and matchmaking quality, balancing player experience, fairness, reliability, and operational constraints. Partner closely with product managers, designers, analysts, and engineers to shape ambiguous opportunities into clear technical plans and shipped player-facing features. Translate gameplay, behavioral, and product telemetry into reliable signals and evaluation frameworks. Design and run experiments to evaluate model quality, player impact, and system tradeoffs. Work directly with game and service engineers to integrate models into League systems and services, including helping define instrumentation and telemetry when needed. Operate independently across multiple partner groups, driving multi-month work with limited day-to-day oversight. Contribute to the ML engineering community at Riot through peer reviews, documentation, craft standards, and shared learnings. Help establish robust monitoring, observability, and support practices for live ML systems as they scale. Required Qualifications: Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience. 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles. Evidence that your modeling choices have been adopted beyond your immediate team — whether through reusable patterns, shared architectures, or influence on how others approach problems. History of working with complex or unconventional data sources where off-the-shelf feature engineering doesn’t apply. Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction. Comfort with ambiguity — you’ve shipped in situations where the success metric, the right approach, or both were unclear at the start. Track record mentoring engineers across roles and levels; evidence of raising the bar for people around you. Excellent written and verbal communication. Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is a plus. Experience bridging research and production — translating papers or prototypes into reliable shipped systems — is a plus. Familiarity with ML platform components (model serving, feature stores, ML observability) is a plus. Passion for player experience, games, or creative technology. Desired Qualifications: Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products. Experience with matchmaking, recommendations, multi-objective optimization, or other systems that must balance competing goals. Familiarity with causal inference, uplift modeling, contextual bandits, reinforcement learning, or other approaches useful for adaptive player experiences. Experience integrating models into latency-sensitive or high-reliability production environments. Experience defining telemetry or instrumentation needs in close partnership with software engineers. Familiarity with responsible AI practices, including fairness, safety, transparency, and operational trustworthiness. Comfort collaborating across a central craft organization and an embedded product team model. For this role, you’ll find success through: Strong applied ML craft Independent execution in ambiguous spaces Thoughtful collaboration with product, design, engineering, and Insights partners Decision-making that prioritizes player value and long-term system health For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.