Location: New York City, New York
What you’ll be part of
Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster.
The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.
Who we want
This is a role for someone who wants to work on a hard problem, deliver real impact and be pushed to be the best version of themselves. As an Associate Applied Scientist, you’ll be embedded in the Applied Science team. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them.
This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund.
What you will do
Ship production algorithmic systems end-to-end, from problem framing to launch to iteration
Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks
Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship
Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for
Bring new ideas to your work, experimenting with approaches beyond the obvious to get better results
Produce rigorous, well-validated work others can rely on, and review your peers' code and analyses
Build a deep understanding of the healthcare economy and Garner's place in it
To make the role concrete, here are two problems on our near-term roadmap:
Provider tiering optimization.
Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions.
Member engagement model.
Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care.
What we're looking for
A Bachelor's or Master's degree (in progress) in Computer Science, Mathematics, Statistics, Economics, or a related STEM field, with an expected graduation date of Fall 2026 or Spring 2027.
A bias toward action, quickly translating ideas into working prototypes to test approaches.
Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them.
A solid command of the methods your work calls for and the data your models depend on.
Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem.
Strong communication skills, with the ability to present your work clearly to senior stakeholders.
A desire to do work that matters: and to be held to a high standard while doing it. You're energized by a mission with measurable outcomes, not just good intentions. You engage with AI tools as part of how you do your best work, and hold yourself accountable to outcomes that matter beyond your team’s metrics.
What you’ll get here
You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission.
At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose.
Technologies we use
Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment.
Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.
Compensation
The target base comp range for this role is $150,000. Individual compensation will depend on qualifications, skills, and applicable laws.
In addition to base compensation, this role is eligible to participate in our equity incentive plan and benefits package, including: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health, and more.
Few roles offer this combination: work that is genuinely hard, a mission that is genuinely important, and a team that is genuinely invested in your success. If that’s what you’re looking for, we’d love to meet you.
Fraud and Security Notice:
Please be aware of recent job scam attempts. Our recruiters use
getgarner.com
and
garnerhealth.com
email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement
here
and to
candidateprotection@garnerhealth.com
.
Equal Employment Opportunity:
Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.
Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at
talent@garnerhealth.com
.
Задачи:
— Разработка и внедрение автономных ИИ-агентов и генеративных моделей (LLM, мультимодальные системы) для ускорения проектирования, разработки и развертывания ERP-систем, а также создания интеллектуальных интерфейсов автоматизации — от генерации кода до обработки естественно-языковых запросов пользователей.
— Создание ИИ-сервисов и автономных агентов для автоматизации бизнес-процессов.
— Обучение и тонкая настройка LLM (Large Language Models) и других ML-моделей под задачи компании.
— Анализ данных, разработка алгоритмов для прогнозирования, оптимизации и принятия решений.
— Интеграция ИИ-решений в существующие продукты компании.
— Участие в научно-исследовательских проектах, публикация результатов (при желании).
Требования:
— Опыт в работы в IT от 5-ти лет.
— Опыт работы в области Machine Learning, Deep Learning или Data Science от 3-ех лет.
— Знание Python и ряда библиотек для ML/DL таких как TensorFlow, Pandas, PyTorch, scikit-learn и др.
— Опыт работы с NLP, LLM (GPT, Llama, Mistral и т. д.), RAG, автономными агентами.
— Понимание принципов работы ERP-систем (желательно).
— Умение работать с большими данными (SQL, NoSQL).
— Знание методов обучения с подкреплением (RL) — будет плюсом.
— Английский язык (уровень для чтения технической документации).
Условия:
— Официальное оформление и стабильный доход по ТК РФ.
— Все льготы и преимущества работы в аккредитованной ИТ-компании.
— Программа ДМС с первых дней.
— Работа в офисе с гибким началом рабочего дня.
— Московский офис в шаговой доступности от м. Динамо, оборудованный всем необходимым для комфортной работы и отдыха.
ОфисМосква
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
Location: United States
Workplace: Remote
At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀
Machine Learning Engineer, Ranking & Retrieval
ClickUp is seeking an experienced Machine Learning Engineer to join our Search team. You'll own the ML systems that power search relevance for millions of users and ground our AI in the right context.
Your Impact at ClickUp
You'll own the full ML lifecycle for ranking and retrieval, from training through deployment and production serving. Our search indexes large-scale, user-generated content across a multi-tenant platform where permissions-aware retrieval is critical. You'll build the systems that decide what surfaces first.
Core Responsibilities
- Train, deploy, and serve ranking models in production, owning the full ML lifecycle
- Build ranker features, training pipelines, and offline evaluation frameworks
- Design and scale hybrid retrieval combining lexical and vector search (including HNSW with disk offloading)
- Run embedding inference at billions-of-documents scale
- Improve query understanding through intent modeling and query expansion
- Build permissions-aware retrieval that respects multi-tenant boundaries
- Create measurement frameworks to evaluate and improve search quality
- Collaborate with Search Infrastructure, AI, and backend teams to integrate ranking improvements across the platform
Technical Requirements
- Bachelor's degree in Computer Science, Machine Learning, or related field
- 5+ years of ML engineering experience focused on ranking, retrieval, or information retrieval
- Proven full ML lifecycle ownership: training, deploying, and serving models in production
- Hands-on ranker model training: feature engineering, pipelines, offline evaluation
- Experience building hybrid (lexical + vector) retrieval systems
- Experience running embedding inference at large scale
- Strong query understanding fundamentals: intent modeling, query expansion
Preferred Qualifications
- Permission-aware retrieval and multi-tenancy experience
- Indexing large-scale user-generated content (not small or static datasets)
- Hands-on experience with OpenSearch or Elasticsearch
- Sharding, index management, and real-time ingestion at scale
- Background in NLP, semantic search, or agentic retrieval
- Experience with TypeScript in backend systems
Join Our Mission
At ClickUp, we're committed to building the future of productivity software. As our Search Engineer, you'll play a crucial role in shaping the core functionality of our platform and driving our AI initiatives forward. Join us in our mission to make the world more productive!
#LI-REMOTE
Equal Opportunity Employer
ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
Privacy Notice
ClickUp collects and processes personal data in accordance with applicable data protection laws. You can find further details by viewing our Global Candidate Privacy Notice. https://clickup.com/careers-terms/candidate-privacy-notice
If you are a Philippine Job Applicant, please also see our Philippine Data Privacy Notice https://t333.s.clickup-attachments-stg.com/t333/d9a53ce2-6d2c-48cd-84b1-ed4e525f9613/Philippine%20Data%20Privacy%20Notice_2024.pdf?view=open for further details.
Visa Sponsorship
Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions.
Fraud Alert
ClickUp Talent Acquisition will only initiate contact via an @clickup.com http://clickup.com email or through our official careers portal on clickup.com http://clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com.
AI Processing Notice
ClickUp may use artificial intelligence and machine learning technologies to help review and screen candidates' employment applications against role-related criteria. These tools support, but do not replace, human decision‑making. If you have questions or need an accommodation in the recruitment process, please contact us at AskPeople@ClickUp.com.
Location: Europe
We are toogeza, a Ukrainian recruiting company focused on hiring talent and building teams for tech startups worldwide. People make a difference in the big game, and we may help find the right ones.
Currently, we are looking for a Machine Learning Engineer — Physics AI for Zibra AI.
Zibra AI is a deep-tech company building advanced technologies for working with large-scale 3D data. The team has a strong background in computer graphics and data compression and is now expanding its technology into industrial simulation and Physics AI.
The company is developing a new data infrastructure layer that makes massive scientific and simulation datasets significantly easier to store, transfer, visualize, and use for AI training.
You will work at the intersection of Physics AI, scientific computing, ML systems, and data compression. A major part of the role is to benchmark our codec across different model architectures, study how compression affects accuracy and training efficiency, and explore new approaches to training directly in compressed representations.
WHAT YOU WILL DO
- Benchmark our compression technology across a wide range of Physics AI architectures and datasets.
- Run large-scale experiments for CFD, turbulence, weather, engineering, and other scientific ML workloads.
- Measure the impact of compression on:
- model convergence and final accuracy;
- training throughput;
- GPU utilization;
- CPU and data-loading overhead;
- storage and network requirements.
- Compare compressed-data training against conventional pipelines and alternative compression methods.
- Research training directly in compressed or partially decoded representations.
- Explore compression-aware sampling, augmentation, tokenization, and model architectures.
- Design rigorous, reproducible benchmark methodology.
- Integrate compressed datasets into PyTorch and distributed training workflows.
- Turn experimental results into product recommendations and research directions.
- Write technical reports, benchmark publications, blog posts, and academic papers.
- Collaborate with external research groups and industrial partners on joint evaluations.
WHAT WE ARE LOOKING FOR
- Hands-on experience with Physics AI / scientific machine learning is required.
- Experience training models on simulation or physical-science datasets.
- Strong practical experience with PyTorch and modern deep-learning workflows.
- Familiarity with architectures such as:
- neural operators;
- mesh GNNs;
- transformers for physical systems;
- surrogate models;
- foundation models for science;
- PINNs or related methods.
- Experience with large 3D/4D datasets such as volumetric grids, meshes, point clouds, or spatiotemporal fields.
- Good understanding of GPU training performance, data loaders, profiling, and distributed training.
- Strong experimental methodology and ability to design controlled benchmarks.
- Ability to analyze how numerical approximation and preprocessing affect model quality.
- Strong Python and scientific-computing skills.
- Ability to communicate research results clearly in written technical form.
NICE TO HAVE
- Experience with PhysicsNeMo or similar scientific ML frameworks.
- Background in CFD, FEA, climate, turbulence, combustion, or computational physics.
- Knowledge of lossy compression, quantization, numerical error analysis, or signal processing.
- Multi-GPU or multi-node training experience.
- Previous academic publications in ML, scientific computing, compression, or related fields.
If this role sounds like a fit — we’d love to hear from you! Just send over your CV and anything else you’d like us to consider.
We’ll review everything within five working days, and if your background matches what we’re looking for, we’ll get in touch to set up a call and get to know each other better.
Задачи:
• Владение архитектурой и поставкой сервисов прогнозирования, ранжирования и рекомендаций.
• Проектирование воспроизводимых рабочих процессов обучения, оценки, мониторинга и переобучения.
• Перевод целей продукта в измеримые задачи машинного обучения, эксперименты и релизы.
Требования:
• Более 5 лет профессионального опыта в разработке программного обеспечения или машинного обучения с производственными системами.
• Продвинутые знания Python и SQL; сильные навыки в статистике, экспериментах и оценке моделей.
• Опыт работы с оркестрацией, контейнерами, облачной инфраструктурой, наблюдаемостью и CI/CD.
• Умение отлаживать ошибки данных и моделей.
• Опыт в прогнозировании, ранжировании, рекомендациях, оптимизации или другой прикладной области машинного обучения.
• Владение английским языком для технической документации и межфункционального взаимодействия.
Ipotekabank — банк в Узбекистане, входящий в международную финансовую OTP Group, специализирующийся на ипотечном и розничном кредитовании.
Вакансии:
- Principal QA Engineer (AI Engineering)
- Senior QA Engineer
- Senior QA специалист
Офис в Ташкенте.
Location: San Mateo
Workplace: Hybrid
Lavendo partners with startups and high‑growth companies to help them hire top‑tier sales, GTM, and technical talent. This role is with one of our clients; we’ll share full details about the company and interview process as we get to know you and confirm mutual fit.
ABOUT THE COMPANY
Our client is a fast-growing AI infrastructure company that has built a state-of-the-art data curation platform used to train some of the most demanding deep learning models in the world. Their technology automatically curates and optimizes petabytes of training data — algorithms that are modality-agnostic and require no labels — to make model training dramatically faster and more efficient. Customers see results that speak for themselves: 7x–40x faster training, model performance equivalent to training on 10x more raw data, and smaller models (with less than half the parameters) that outperform larger ones and substantially cut deployment costs.
Backed by $57.5M raised across Seed and Series A, with investors including Microsoft, Amazon, Felicis, and AI luminaries like Geoff Hinton, Yann LeCun, and Jeff Dean, our client has built a lean team of ~60 people. This is a company already proving out results with real enterprise customers.
THE MISSION
Foundational models are only as good as the data they're trained on. Our client exists to close the gap between raw data and truly optimized training data — without requiring labels, without adding compute cost, and without forcing customers to compromise on model quality. Every strategic account this team lands is a chance to prove that better data curation, not just bigger models, is the unlock the industry has been missing.
THE OPPORTUNITY
As Forward Deployed Engineer (Post-Sales), this is a rare role at a company small enough that your fingerprints will be on every major account, and well-funded enough that the roadmap, the compensation, and the customer roster are all already real. You'll be one of the founding members of the post-sales technical org, working directly with strategic enterprise accounts to take them from signed contract to production deployment — and shaping the playbooks that the next wave of hires will use.
If you've been looking for a role that lets you stay hands-on with distributed systems and model training while also owning the high-stakes customer relationships, this is that role.
WHAT YOU'LL DO
- Own end-to-end onboarding, deployment, and production rollout of the platform for strategic enterprise accounts — from first technical conversation through stabilization and beyond
- Serve as the primary technical point of contact for your accounts, building long-term relationships and driving adoption across complex on-prem and hybrid environments
- Design scalable, secure workflows spanning compute, storage, networking, and distributed systems across AWS, GCP, Azure, and on-prem Kubernetes
- Translate ambiguous, real-world customer requirements into concrete technical architecture — there's no pre-written spec waiting for you; you help define the problem
- Build the processes and playbooks for post-sales deployment from 0→1, knowing that what you build now becomes the foundation for the team scaling behind you
- Partner cross-functionally with Sales, Engineering, and Research, relaying field learnings that directly shape the product roadmap
- Travel to customer sites as needed (roughly 15%) to support critical deployments and high-stakes engagements
WHAT YOU BRING
- 5–10 years of experience in a post-sales individual-contributor capacity, owning customer deployments as a Forward Deployed Engineer, Post-Sales Solutions Engineer/Architect, Implementation Engineer, Machine Learning Engineer, or customer-facing engineer
- Hands-on machine learning and model training experience — you understand pre-training, mid-training, and post-training concepts, not just how to call an API
- Real experience deploying software into enterprise customer infrastructure
- Strong Python proficiency (required) and AWS proficiency (required)
- A track record of owning the full post-sales enterprise lifecycle — onboarding, optimization, and everything in between
- Experience building post-sales processes or playbooks from scratch
- Comfort operating in ambiguous, high-stakes, and fast-moving environments — startup or high-ambiguity experience is a plus
- A technical degree (CS or equivalent)
- A demonstrated ability to translate messy, ambiguous customer requirements into clean, concrete architecture
- Willingness to travel to customer sites as needed (roughly 15%)
- Nice to have: exposure to multi-cloud environments (GCP, Azure), on-prem or hybrid Kubernetes, or broader ML tooling — not required, but a strong differentiator
Note: This role is built for people who bring both deep post-sales, customer-facing deployment experience and hands-on ML/model training expertise with measurable results. If you're strong in only one of these two areas, this likely isn't the right fit — but we'd still encourage a conversation if you're close.
WHY JOIN?
- Compensation: $230,000–$300,000 base plus extremely competitive equity
- Health benefits: 100% covered health benefits (medical, vision, and dental)
- 401(k): 401(k) plan with a 4% company match
- Time off: Unlimited PTO
- Wellness: Annual $2,000 wellness stipend
- Learning & development: Annual $1,000 learning and development stipend
- In-office perks: Daily lunches and snacks provided in the office
- Relocation: Relocation assistance for employees moving to the Bay Area
- Real ownership: With a lean team of ~60 people, you'll be the technical owner of some of the company's most strategic customer relationships
- Proven in production: You're joining a company whose product already delivers 7x–40x faster training and measurable model performance gains for real customers
- Backing that matters: $57.5M raised from Microsoft, Amazon, Felicis, and AI pioneers including Geoff Hinton, Yann LeCun, and Jeff Dean
- Hybrid setup: 4 days per week in the San Mateo, CA office, with the rest of the team building alongside you in person
- Visa support: Open to visa transfers, including OPT and H-1B transfers
INTERVIEWING PROCESS
- Initial Screen — Assesses background, interest, culture fit, comp expectations, and logistics
- Hiring Manager Technical Screen (30 min) — Evaluates distributed systems experience, cloud infrastructure (AWS/GCP/Azure), Kubernetes, and post-sales/customer-facing mindset
- Coding Screen (1 hour) — Live coding in Python with end-to-end debugging
- On-site (3.5 hours) — System design/architecture discussion, technical deep dives, behavioral/stakeholder scenarios, and cross-functional interviews with Sales, Engineering, and Research
We are proud to be an equal opportunity workplace and consider all qualified applicants without regard to race, color, religion, national origin, age, sex, marital status, ancestry, disability, genetic information, veteran or military status, gender identity or expression, sexual orientation, or any other characteristic protected by law.
Location: Zürich, CH
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 Team
The Security team protects Anthropic's AI systems and maintains the trust of our users and society.
About the Role
At Anthropic, we are pioneering new frontiers in AI that have the potential to greatly benefit society. However, developing advanced AI also comes with risks if not properly safeguarded. As a Research Engineer, you'll help to safely advance the capabilities of our models in incident response, security analysis, vulnerability remediation, and other areas of defensive cybersecurity.
This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic.
This role requires ML experience combined with domain expertise in cybersecurity. You might be an ML researcher with a history of cybersecurity work, or a security professional with a background in ML.
Responsibilities
Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions
Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest
This role may require participation in an on-call rotation.
You may be a good fit if you
Have experience with machine learning.
Have experience in cybersecurity research.
Have strong software engineering skills.
Can balance research exploration with engineering implementation.
Are passionate about AI's potential and committed to developing safe and beneficial systems.
Strong candidates may also have
Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work.
Experience participating in or building CTF competitions and cyber ranges.
Academic research experience in cybersecurity or other experimental & research background.
Familiarity with RL techniques and environments.
Familiarity with LLM training methodologies.
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.
Задачи:
— Design and build AI/ML-powered features and systems
— Work with LLMs, APIs, and model integrations
— Develop and optimize AI pipelines for real-world use cases
— Collaborate with product and engineering teams
— Turn complex AI ideas into scalable production features
Требования:
— Strong experience with Python and AI/ML frameworks
— Hands-on experience with LLMs (OpenAI, LangChain, etc.)
— Understanding of model integration, prompting, or fine-tuning
— Experience with APIs and backend systems
— Strong problem-solving mindset and startup experience
Location: Москва
Мы развиваем GigaChat и ищем сильного ML-инженера в команду online RL. Это роль для человека, который умеет доводить исследовательские идеи до работающих решений: проектировать и запускать эксперименты, строить надёжные пайплайны обучения и добиваться реального роста качества модели.
Нам нужен не просто исполнитель задач, а инженер с сильным исследовательским мышлением, который способен самостоятельно разбираться в сложных проблемах, предлагать новые идеи и решения и нести ответственность за результат.
Обязанности
* разрабатывать и улучшать методы online RL
* реализовывать и дорабатывать подходы post-training и online RL
* проектировать и проводить эксперименты: формулировать гипотезы, подбирать конфигурации, анализировать результаты не только на уровне метрик, но и на уровне причин
* разбираться, почему модель стала лучше или хуже, насколько устойчив результат и можно ли его масштабировать на другие домены и типы задач
* следить за state-of-the-art: читать статьи, воспроизводить результаты, адаптировать лучшие подходы под наши задачи и инфраструктуру
* строить и развивать инфраструктуру обучения
* разрабатывать и поддерживать пайплайны online RL: от генерации rollout'ов и сбора reward-сигналов до обновления весов модели
* обеспечивать воспроизводимость экспериментов: версионирование данных, конфигов, чекпоинтов, контроль деградаций
* оптимизировать throughput и эффективность использования GPU: distributed training, параллелизм, профилирование узких мест
* выстраивать связку между моделью, средами исполнения, верификаторами и reward-моделями так, чтобы новые идеи можно было быстро проверять
* работать с данными и системой оценки качества
* участвовать в проектировании и реализации reward-сигналов: rule-based верификаторы, reward-модели, LLM-as-a-judge, execution-based проверки
* строить и улучшать пайплайны подготовки данных: фильтрация, дедупликация, балансировка, контроль утечек
* анализировать ошибки модели, выявлять систематические слабые места и формировать целевые обучающие выборки для их устранения
* тесно взаимодействовать с исследователями, другими инженерами, командами данных и инфраструктуры
* брать на себя ответственность за целые куски системы: от идеи до результата в проде
* делиться знаниями, участвовать в код-ревью, помогать поднимать общую планку качества.
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Требования
* отличное владение Python и PyTorch
* практический опыт в LLM post-training: RLHF, online RL, DPO или смежных направлениях
* опыт проведения ML-экспериментов от начала до конца: постановка гипотезы → реализация → анализ → выводы
* понимание distributed training: Data Parallel, FSDP, DeepSpeed или аналоги.
* Умение писать чистый, надёжный, production-ready код
* способность разбираться в сложных системах и самостоятельно находить и устранять узкие места.
Условия
* возможность выбрать удобный формат работы: гибрид или офис
* ежегодный пересмотр зарплаты, годовая премия
* корпоративный спортзал и зоны отдыха
* более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
* расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа
* ипотека выгоднее до 7% для каждого сотрудника
* бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров
* вознаграждение за рекомендацию друзей в команду Сбера.
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