Вакансии

Location: Cardiff, London or Remote (UK) 🚀 We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us. With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers! We’re not about selling products - we want to solve problems and change lives through Monzo ❤️ 📍London or Remote (UK)  | 💰 £79,000 - £93,000 + Incentive Awards tied to your performance + Benefits 🌟 About the team: Our Borrowing (lending) business is growing rapidly across both existing products and with the planned launch of new initiatives. As our lending portfolio grows, our second line of defence Credit Model Validation team is growing with it - and we’re looking for a Credit Model Validation Manager to join us! Our Credit Model Validation team is crucial in providing independent oversight and challenge to our model development teams in the first line of defence to help Monzo grow safely and ensure that the models we use deliver good outcomes for our customers. We’re looking for someone (maybe you?!) to join our validation squad and provide expert independent validation and oversight across our suite of credit models, covering critical business disciplines such as credit strategy and impairment. You’ll have a particular focus on models used for customer-level decisioning and defining our credit strategies - such as underwriting scorecards, origination PD models and Net Present Value (NPV) / unit economics models - across all our lending products. Strong data and analytical skills are a must, as is deep expertise in credit risk modelling. You’ll have hands-on experience in either model validation or model development within a financial services environment, including practical experience using Python to develop, validate or analyse machine learning and statistical models. . In-depth knowledge of credit decisioning models would be a strong plus. 🔑 You’ll play a key role by… Leading hands-on independent validation of models used for credit decisioning within our Borrowing strategies - covering a range of machine learning, decision science and NPV models. You will be ensuring that models are fit for purpose, appropriately governed, explainable and performing as expected Supporting oversight and validation of broader credit risk models, including IFRS9, stress testing and economic response models Developing deep understanding of Monzo’s credit models, and using this to provide impactful input and challenge to model developers to improve our modelling capabilities Reviewing ongoing model performance monitoring to ensure our lending models perform within our risk thresholds, and providing support to our first line of defence teams when action is required Performing statistical analyses to independently assess model performance, including developing challenger models where appropriate to test model assumptions and strengthen validation conclusions Improving our Model Risk Framework, including overseeing our policies, standards and procedures to help embed best practice across the business. This means making sure we follow up-to-date regulatory guidelines, and adapt our processes to make sure they remain relevant as modelling techniques evolve Writing clear and impactful model validation reports, and presenting the findings and actions in relevant committees Partnering with colleagues in the first line of defence credit strategy and modelling teams - and wider risk teams across Monzo - to support the embedding of our model risk framework and establishing consistent validation standards across the organisation Embracing new and innovative technology, including leading the adoption of AI tools to enhance our model validation approach 🤩 We’d love to hear from you if… Have a strong background and practical experience in credit model development and/or validation - in particular with exposure to machine learning or statistical models used in credit decisioning, such as decision science scorecards, origination / underwriting PD models, and NPV / unit economics modelling Are data driven, with strong analytical skills and deep understanding of statistical techniques for credit and model risk. Being able to get hands on with data is critical - so strong SQL and Python skills are essential, and experience with data visualisation tools such as Tableau, Looker or similar is a plus! Understand how models are used in real-time decisioning and lending strategies, and how to balance commercial and risk outcomes Have strong written and verbal communication skills, and are able to articulate problems and solutions using clear and simple language. This is particularly important for breaking down complex issues and priorities to non-experts Have a track record of building and maintaining trusting relationships with stakeholders at all levels, and can provide effective and respectful challenge Are impact-driven, and proactive in identifying issues and opportunities to improve outcomes for both our customers and our business Are passionate about technology, and what we’re doing here at Monzo excites you! Our technology stack We rely heavily on the following tools and technologies (note we do not expect applicants to have prior experience of all them): Google Cloud Platform for all of our analytics infrastructure dbt and BigQuery SQL for our data modelling and warehousing Python for data science and machine learning Go to write our application code AWS for most of our backend infrastructure The interview process: Our interview process usually involves up to four main stages via Google Hangouts: An initial call with our recruiter A call with our Senior Model Validation Manager, the hiring manager for the role A role-specific call with colleagues from our credit modelling teams. We’ll share a short case study in advance, but you won’t need to submit a written response; we’ll use it to guide the discussion during the interview. A behavioural interview 🙌 What’s in it for you 💰 £79,000 - £93,000 + Incentive Awards tied to your performance benefits. 📍 This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). ⏰ We offer flexible working hours and trust you to work enough hours to do your job well, and at times that suit you and your team. 📚 £1,000 learning budget each year to use on books, training courses and conferences. 🏡 We will set you up to work from home; all employees are given MacBooks and for fully remote workers we will provide extra support for your work-from-home setup. ➕ Plus lots more! Read our full list of benefits . We have some guidelines on using Artificial Intelligence (AI) to ace an application and interview at Monzo. You can read them here . We’ll close this role once we have enough applications for the next stage, so please apply as soon as you can - we don’t want you to miss out! LinkedIn Tags: #LI-TK1 Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog , 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report. We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don't need full or birth names at application stage 😊

Location: Argentina; India; Philippines; Nigeria APPLICATION INSTRUCTIONS: If you’ve already completed the 20four7VA Recruiting Process—including the Final Interview—or have previously been contracted by a 20four7VA Client, please make sure to apply via the 20four7VA Team Portal for faster processing. Log in using your 20four7VA-issued email address to access the portal. If you’ve just logged in to the Team Portal, please proceed to review the Independent Contractor Opportunity outlined below. 💡 Quick Tip: Upload your resume first—this will automatically populate most of the fields in the application form. Let the magic happen! If you're interested in this opportunity, click APPLY TO POSITION to proceed. ******************************************************************** Title : Independent Contractor – AI & ML Developer (Remote). About 20four7VA 20four7VA is a global leader in providing virtual staffing solutions. We are expanding our technology team to integrate AI-driven automation into customer support and ticketing systems. We are seeking a skilled AI & ML Developer with expertise in NLP, chatbot development, and intelligent automation. Work Hours: 20 hrs/week (potential to go full-time) Schedule: 9:30 AM – 2:30 PM EST (Flexi, preferably weekdays) Key Responsibilities 1. Natural Language Processing (NLP) & AI Model Development Design and develop NLP models for chatbots, sentiment analysis, and document processing. Implement text classification, named entity recognition (NER), summarization, and translation. Fine-tune and deploy pre-trained LLMs (e.g., GPT-4, BERT, LLaMA, T5). 2. AI-Powered Chatbot Development Build and deploy intelligent chatbots for customer support and ticketing automation . Implement intent recognition, entity extraction, and context-aware dialogue flows. Integrate chatbots with WhatsApp, Slack, MS Teams, and WebChat. Enable multi-turn conversations and proactive AI-driven responses. 3. Ticketing System Optimization Develop AI models for ticket categorization, prioritization, and auto-routing. Implement AI-powered auto-response generation for FAQs. Apply reinforcement learning for optimizing resolution workflows. 4. Data Processing & Annotation Preprocess and clean ticketing logs, chat transcripts, and emails. Apply tokenization, lemmatization, and embeddings for improved NLP accuracy. Enhance supervised models through dataset labeling and annotation. 5. API & System Integration Build APIs for AI-driven chatbot and ticketing automation. Integrate with CRMs and ITSM tools (e.g., ServiceNow, Zendesk, Freshdesk). Create real-time NLP analytics dashboards for chatbot performance tracking. 6. AI Model Optimization & Evaluation Conduct A/B testing, hyperparameter tuning, and model benchmarking. Apply evaluation metrics (F1-score, BLEU, ROUGE, perplexity). Optimize for low-latency, real-time interactions. 7. Research & Continuous Improvement Stay updated with the latest in LLMs, generative AI, and conversational AI . Experiment with Gemini, Claude, Mistral , and other cutting-edge models. Contribute to innovation in AI-driven automated support systems. 8. Security, Compliance & Ethics Ensure compliance with GDPR, CCPA, HIPAA (where applicable). Implement bias detection, fairness, and ethical AI practices. Secure models against adversarial attacks and vulnerabilities. 9. Collaboration & Documentation Work with IT, customer support, and automation teams. Document architectures, workflows, and training pipelines. Conduct internal training sessions for teams on AI automation tools. Technologies & Tools Programming: Python, Django, Flask AI/ML: OpenAI API, Hugging Face Transformers, TensorFlow, PyTorch Frontend/Integration: React, REST APIs, Webhooks Databases: MySQL, MongoDB, PostgreSQL Platforms: NLP/Conversational AI platforms (Dialogflow, Rasa, LangChain, etc.) Preferred Experience AI-powered chat-based support systems . Automated ticketing workflows using ML. Hands-on experience with large-scale chatbot deployments . Exposure to enterprise-level CRM/ITSM integrations . Why Join Us? Opportunity to steer AI innovation in global support systems. Flexible schedule with potential to grow into full-time role . Collaborative remote-first culture with focus on automation, innovation, and impact .

Обязанности: - Запускать и развивать сервисы, которые основаны на ML-моделях и LLM - Работать над проектами от идеи до продакшена: анализировать потребности, проектировать архитектуру, разрабатывать и внедрять проекты - Интегрировать модели в удобные для сотрудников интерфейсы - Вместе с командой влиять на технические решения и стандарты, внедрять лучшие практики Требования: - Опыт работы с Python 3 - Опыт работы с FastAPI и базами данных PostgreSQL, контейнерами в Docker и кластерами Kubernetes - Умение писать тесты на Pytest - Интерес к миру ML на уровне доменной области - Самостоятельность и готовность нести ответственность за проект

Location: Tallinn, Estonia Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (Applied AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: A Unique Opportunity for a career in a global, fast-growing company. Attractive remuneration package based on qualifications and experience. Opportunities to learn and grow through our “ Employee Training & Development program”. Stebby Sports Compensation or Confido Health Compensation. Multiple events to bond with the team and the group. Birthday and Loyalty benefits . What to expect from our recruitment process: Interview with HR Assessment task Interview with hiring managers Final interview with top management Make your next Career step and apply NOW! *Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Location: Limassol, Cyprus Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: A Unique Opportunity for a career in a global, fast-growing company. Attractive remuneration package based on qualifications and experience (including 13th salary and Discretionary Bonuses to reward exceptional performance). Opportunities to learn and grow through our “ Employee Training & Development program”. Medical Insurance Cover , which includes Outpatient, Inpatient, and Dental Care. Multiple events to bond with the team and the group through Quarterly/Semestrial Team Activities for all the Company. Participation in our welfare investment and savings plan through our Provident Fund Scheme. Birthday and Loyalty benefits . Collaboration with SportBenefit. What to expect from our recruitment process: First interview with hiring managers or an HR call Task Assessment Technical Interview or home assignment Final interview with top management Make your next Career step and apply NOW! * Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Задачи: - Проектировать и согласовывать архитектуру AI-агента, приложений и интеграций. - Развивать и разрабатывать backend-компоненты AI-агента, общие библиотеки и интеграционные взаимодействия. - Реализовывать решения на базе LLM, ReAct и RAG, включая поиск и подбор релевантных «шагов» из существующей библиотеки. - Интегрировать систему с внутренними сервисами, репозиториями, базами знаний и корпоративной инфраструктурой. - Формировать требования для построения CI/CD, мониторинга, логирования, трассировки и контроля качества для AI-агента. - Проводить ревью кода, поддерживать код-стандарт и участвовать в командных коммуникациях. - Взаимодействовать с специалистами по автоматизированному тестированию, QA-инженерами и разработчиками для уточнения требований и сценариев использования. - Участвовать в разработке пользовательской поверхности решения, в формате IDE plugin. Требования: - Опыт работы от 3-4 лет в разработке. - Высшее образование. - Практический опыт построения RAG-систем и работы с векторными хранилищами, желательно PostgreSQL + pgvector. - Опыт работы с LangChain, Python. - Опыт разработки ReAct агентов. - Понимание принципов ООП, SOLID и паттернов проектирования. - Понимание базовых принципов многопоточного и асинхронного программирования, включая GIL и asyncio. - Практические знания и навыки работы с LLM. - Опыт работы с инструментами контейнеризации приложений, включая Docker. - Лидерские качества: умение принимать решения и брать на себя ответственность. Условия: - Возможность участия в интересных проектах. - Возможность профессионального и карьерного роста в компании. - Опыт работы в команде профессионалов. - Специальные тарифы для сотрудников в спортивные клубы и языковые курсы и пр. - Удаленный и гибридные форматы работы.

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. About the role We are seeking an exceptional Staff Applied Researcher to join our Applied Science team. You will be responsible for building the algorithmic systems that power Garner — determining how we evaluate providers, make recommendations, and optimize for outcomes across cost, quality, and access. You will be responsible for turning ambiguous, real-world problems into systems that deliver measurable impact, defining the objective functions, metrics, and logic that drive our product. You will own these systems end-to-end, from problem definition through production and ongoing performance. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them 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, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Review applied science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three 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. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. 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. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred 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 Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, including at the executive level, with a track record of driving alignment across an organization A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback 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. Why this role This is a unique opportunity to work on high-impact search problems in healthcare, helping shape how members find better care through algorithmic systems that directly influence healthcare outcomes. Compensation Transparency: The target base comp range for this position is $260,000 – $382,000 . Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. 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 .

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 RL Teams Our Reinforcement Learning teams play a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of our latest Claude models. Our work spans several key areas: Developing systems that enable models to use computers effectively Advancing code generation through reinforcement learning Pioneering fundamental RL research for large language models Building scalable RL infrastructure and training methodologies Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely. This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact. You may be a good fit if you: Have strong software-engineering skills and deep Python expertise, including async/concurrent programming Are comfortable owning systems end to end and debugging across the stack Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results Care about code quality, testing, and performance Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may also have: Experience with reinforcement learning, RLHF, post-training, or LLM finetuning Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling Background in program analysis, testing, verification, compilers, or formal methods Experience with PyTorch and large-scale distributed training; performance profiling and optimization of ML systems CUDA / GPU or TPU kernel experience and accelerator-performance intuition Experience with virtualization and sandboxed code execution environments Related roles If your background leans toward one of these areas specifically, you may also want to look at these postings: Research Engineer, Performance RL (Reinforcement Learning) — teaching Claude to write correct, fast code for accelerators Research Engineer, Universes — long-horizon, ultra-realistic agentic training environments Research Engineer, Cybersecurity RL (Reinforcement Learning) — RL for security-relevant coding capabilities 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: $500,000 — $850,000 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: Montreal DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. Our team works on a variety of high-impact machine learning projects, and we are looking for a Machine Learning Specialist to join us. In this role you will build critical systems for our machine learning models and have the chance to make great impact to our business. As a member of our team, you will be surrounded by cutting-edge technology and experienced machine learning, data and software professionals providing you with the best possible environment to succeed. You will always have interesting new projects to apply your skillset while also becoming an expert on certain areas. Responsibilities: Design and implement scalable and performant systems to serve machine learning models Optimize training and inference performance of deep learning models Create and maintain feature pipelines, feature stores, model stores Monitor production models and setup retraining infrastructure to improve performance Optimize models for deployment on different hardware configurations Build frameworks to promote scalable and reproducible research Drive projects and lead new initiatives Facilitate discussions on code implementation, testing, software architecture best practices Actively engage with others on the team to resolve challenging design and coding issues Collaborate with stakeholders from different departments Qualifications: A bachelor’s or a master’s degree in computer science, software engineering or equivalent 5+ years of experience building software for machine learning systems Strong understanding of machine learning and deep learning concepts Excellent programming and scripting skills Experience with building, validating, deploying, monitoring, and updating production models Experience with infrastructure and tooling for MLOps Strong problem-solving and analytical skills Excellent communication and collaboration skills Experience with machine learning frameworks such as TensorFlow, PyTorch, TensorRT, ONNX Experience with GPU programming using CUDA, OpenCL or similar libraries Experience optimizing inference performance (quantization, profiling, kernel/runtime tuning, serving engines) Experience with one or more of the following is an asset: distributed systems, multi-threaded programming, hardware/software interaction For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . [#LI-KS1]

Location: Bellevue Workplace: OnSite LIFE AT UIPATH The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power. To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose. Could that be you? As a Principal Applied Scientist, you will lead the architecture, research, and productization of these next-generation ML systems, bridging deep research with deployment at scale and shaping the future of enterprise automation. What You Will Do • Define and drive technical strategy for agent-based automation, including how autonomous agents use LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to integrate with the UiPath platform. • Architect, prototype, and deploy advanced ML and AI systems, covering LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems. • Lead the design and implementation of ML infrastructure and services for model training, fine-tuning, large-scale inference, model serving, monitoring, drift detection, continuous learning loops, and ML operations for agentic systems. • Partner closely with product, engineering, design, and go-to-market teams to translate research advances into customer-facing capabilities. • Research state-of-the-art techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, and apply them to automation workflows. • Establish best practices, frameworks, and metrics for evaluating agentic systems, including offline evaluation, simulation environments, human-in-the-loop feedback, A/B testing, and cost, latency, and quality analysis. • Serve as a technical leader and mentor across ML engineering, data science, and software engineering, fostering a culture of experimentation, reproducibility, versioning, and rigorous evaluation. • Represent UiPath in the broader community through publications, open-source contributions, conference participation, and collaboration with academia or ecosystem partners. What We Are Looking For • Advanced degree (MS or PhD preferred) in Computer Science, Machine Learning, AI, or a related field, or equivalent experience. • 10+ years of industry experience in machine learning, including substantial experience building and operating ML systems in production. • Deep expertise in modern ML frameworks and libraries, strong proficiency in Python, and experience integrating ML models into large-scale software systems. • Proven experience with large language models or foundation models, including fine-tuning, data preparation, inference optimization, or model evaluation. • Experience with large datasets, distributed training or inference, scalable architecture design, and optimization for latency, throughput, and cost. • Excellent communication skills with the ability to translate complex ML concepts to product and business audiences. • Demonstrated technical leadership in mentoring, influencing strategy, balancing research with delivery, and driving high-impact outcomes. #LI-MH1 Maybe you don’t tick all the boxes above—but still think you’d be great for the job? Go ahead, apply anyway. Please. Because we know that experience comes in all shapes and sizes—and passion can’t be learned. Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based, and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected. We value a range of diverse backgrounds, experiences and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity, and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes. Additionally, UiPath provides reasonable accommodations for candidates on request and respects applicants' privacy rights. To review these and other legal disclosures, visit our privacy policy https://www.uipath.com/legal/trust-and-security/privacy-policy.

Location: Chicago, IL, USA; New York, NY, USA; San Francisco, CA, USA About the role Chime's Data Science & Machine Learning team builds the models, services, and platforms behind how millions of members manage and grow their financial lives. We're hiring AI/ML Engineers across several teams, Trust & Safety, Lending, Growth, and Foundation Models, and you'll be matched with the team where your background, experience and interests fit best. In this role, you'll build and deploy machine learning systems on some of the richest transactional and behavioral data in fintech, turning it into decisions that protect members from fraud, expand access to credit, and power the personalized experiences and marketing that help millions of members get more out of products like MyPay, Instant Loans, and SpotMe — along with the foundational models the rest of our teams build on. This is a highly applied role: you'll own problems end to end, from framing the question through to a model running in production and moving a metric that matters. In this role, you can expect to Build, train, and deploy deep learning and classical ML models on large-scale financial, transactional, and behavioral datasets Take models from problem framing through to production — training, evaluation, deployment, monitoring, and iteration — and stay accountable for how they behave once they're live Design and improve the systems around the model: feature pipelines, batch and real-time inference, monitoring, and retraining Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into ML solutions, and to make sure the solution is the right one Connect model performance to member outcomes and business metrics, and use experimentation to prove impact Contribute to the shared ML platform, tooling, and standards that the rest of the team builds on Help identify where AI/ML creates measurable impact for members — and where a simpler answer is the better one To thrive in this role, you have Experience building and deploying deep learning models in production, with a solid grasp of architecture choice, training dynamics, and evaluation — and the judgment to model design choices Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you Hands-on experience across the end-to-end ML lifecycle — training, experimentation, optimization, deployment, and monitoring Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark Working knowledge of modern ML infrastructure — AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake — and an MLOps mindset for keeping production systems healthy The ability to operate independently in ambiguous environments, and to communicate clearly with both technical and non-technical partners Nice-to-Have Experience in any one of these is a plus, and helps us match you to the right team. Trust & Safety — fraud, risk, abuse detection, or adversarial modeling Lending — credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements Growth — personalization, recommendation, marketing measurement, or experimentation at scale Foundation Models — transformers on tabular or semi-structured data, large-scale pretraining, ML platform work, or applied research with a publication record #LI-DA1 #LI-Onsite Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. Wage Notice $125,000 — $292,000 USD A little about us At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members. We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress. We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential. Because if we don't—who will? *Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC. What we offer for our full-time, regular employees 🏢 Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. 💻 Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees. ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life. 🏝 Generous vacation policy and company-wide paid days off 🫂 1% of your time off to support local community organizations of your choice 👟 Annual wellness stipend to use towards eligible wellness related expenses 👶 Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents 👪 Access to family planning reimbursement 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: accommodations@chime.com . To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice .

Задачи: — разрабатывать RAG-помощника для пользователей «Авито Подработки»; — автоматизировать работу диспетчеров в «Авито Подработке» с помощью LLM; — участвовать в создании ассистента для «Авито Подработки». Требования: — опыт работы в Data Science / Machine Learning от 3 лет; — опыт построения RAG; — опыт построения AI-агентов; — опыт работы с LLM; — умение писать поддерживаемый, тестируемый и масштабируемый код на Python; — опыт внедрения моделей в продакшен; — умение работать с заказчиками внутри команды и за её пределами.

Location: Hanoi OR Ho Chi Minh City WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. ABOUT WORLDQUANT BRAIN: BRAIN is a group within WorldQuant similar to a traditional B2C fintech setup. Our mission is to simplify quant finance and help participants learn and grow through exposure to quant finance, AI, and ML concepts, and in addition, potentially earn compensation based on the quantity and quality of their ideas. WorldQuant BRAIN provides an advanced crowdsourcing platform for external participants to contribute signals, data, and more. THE ROLE: WorldQuant is seeking Researchers to join the BRAIN team. BRAIN is WorldQuant’s crowdsourcing initiative that allows anyone in the world with the right skills to become a Quant. Research Create and develop Alphas and other utilization algorithms on BRAIN Conduct research on academic quantitative finance literature Identify and design new research domains. Generate ideas to grow the domain Platform Development Analyze current functionalities available to researchers, identify any issues with the platform and provide solutions and recommendations to the BRAIN team Design and test new functionalities and datasets on the BRAIN platform. Business Development and Consultant Engagement Work with BRAIN Strategy and Operations country heads and other Business Development partners to enhance & implement BRAIN business strategy for user and consultant acquisition Conduct training sessions for BRAIN users and consultants Prepare and update training curriculum of BRAIN WHAT YOU’LL BRING: Familiarity and competence in using BRAIN, ex-VRC Research Consultant, or BRAIN Research Consultant preferred Possess or expect a Bachelor’s degree or advanced degree in engineering, science, mathematics, finance or any other related field that is highly analytical and quantitative from a leading university Demonstrated programming experience in one of the following (Java/C++/C/Python/MySQL/SQL Server); knowledge of UNIX preferred Possess a research scientist mind-set; be a self-starter, a creative and persevering deep thinker who is motivated by unsolved challenges Have a strong interest in learning about worldwide financial markets Possess good communication and presentation skills in English WHAT WE OFFER: Competitive and attractive compensation package with clear career road-map – where you feel challenged everyday We offer a strong culture of learning and development: training courses, library, speakers, share and learn events Learn from who sits next to you! Working in WQ you are surrounded by smart and talented people Premium Health Insurance and Employee Assistance Program Generous time-off policy, re-creation sabbatical leave (based on tenure), Trade Union benefits for staff and family Team building activities every month: Local engagement events – Employee clubs: football, ping-pong, badminton, yoga, running, PS5, movies, etc. Annual company trip and occasional global conferences – opportunity to travel and connect with our global teams Happy-hour with tea break, snacks and meals every day in the office! Visit https://www.worldquant.com/brain/ for more information about WorldQuant BRAIN. By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved. WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.

Location: San Francisco, CA 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. We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant. This role sits at the intersection of machine learning, software engineering, and biology — you'll directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development. As a core member of our Life Sciences team, you'll work in a high-impact team that translates deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows. You'll help establish Anthropic as a leader in AI-accelerated biology while shaping how frontier models reason about and execute computational biology tasks. This role offers a unique opportunity to shape how frontier AI models learn to do biology. You'll work alongside some of the world's best AI researchers while tackling problems that matter for human health and scientific understanding. If you're excited about turning your computational biology expertise into model capabilities, we want to hear from you. Key Responsibilities Build and ship agentic tools and integrations that let Claude execute real life science workflows — bioinformatics pipelines, database queries, analysis notebooks, literature review Design and build evaluation benchmarks that measure model capabilities on biology tasks — figure interpretation, bioinformatics, protocol reasoning, literature synthesis Work closely with product and design teams to scope, prototype, and ship features for life sciences users Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements Build and maintain the engineering infrastructure behind our biology product surface — tool scaffolding, data pipelines, eval harnesses Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement Minimum Qualifications Experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting — with an understanding of what real scientific workflows look like and where they break down Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures) A track record of shipping computational tools or pipelines that biologists actually use Comfortable navigating ambiguity and defining problems in a rapidly evolving research environment Able to work independently while collaborating tightly with research, product, and domain-expert teams Results-oriented with a bias toward rapid iteration and measurable impact Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards Preferred Qualifications 5+ years of experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar Ph.D. in computational biology, bioinformatics, bioengineering, CS, or a related quantitative field — or equivalent industry experience Experience with LLM post-training: RLHF, RL from verifiable rewards, SFT data curation, or eval-driven development Direct experience with therapeutic discovery pipelines — target identification, lead optimization, ADMET modeling, or clinical data analysis Familiarity with bioinformatics tooling and pipelines (sequence analysis, structure prediction, single-cell, variant calling, etc.) Experience building agentic systems or tool-use environments Published research in ML for biology, or open-source contributions to computational biology tools Fluency with biological databases (UniProt, PDB, Ensembl, NCBI) and the ability to reason about their schemas and failure modes 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: $300,000 — $320,000 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: Remote Workplace: Remote ABOUT US Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains. As a Senior Machine Learning Engineer in the Search Quality team, you will improve the e-commerce experience for hundreds of millions of users across the world by building the systems that power relevance for global retailers - from fashion and grocery to electronics and hardware. The mission is to measure search quality, push it higher, and catch degradations before the user does. You will achieve this through a blend of fine-tuned LLMs for relevance judgment, real-time models, and deep offline analysis of query logs. ABOUT THE JOB Your primary focus will be relevance evaluation and quality improvements: - LLM-based evaluation. We fine-tune our own models to assess relevance. This involves teaching the model to understand query intent, represent items from messy catalog data, and align model judgments with real user behavior. - Real-time quality in production. Reranking, filtering, signal computation. Latency is a strict requirement, so quality vs speed tradeoff is constant. - Automated quality monitoring and agentic insights. Pipelines to detect degradations and find underperforming patterns. Agent-based systems that generate actionable recommendations for the product data and search configurations. WHAT MAKES THIS INTERESTING - Multi-domain, multi-language, at scale - 40+ languages, 20+ domains. The models need to generalize across all of them - without per-customer rules or overrides. - No universal ground truth. A grocery retailer and a fashion retailer may have different perceptions on what "relevant" means. - Efficiency at scale. Optimizing and scaling LLM inference across our entire customer base. REQUIREMENTS - 4+ years shipping production ML systems - Experience with search, information retrieval, or recommendation systems - Hands-on experience with fine-tuning, evaluation frameworks, and scaling LLM deployments - Strong Python and PyTorch. Fluency in SQL and data orchestration tools (Spark, Airflow) - Experience designing and running A/B tests to validate model impact - Excellent English communication skills - Experience collaborating in cross-functional teams (ranking, product, data engineering) BENEFITS - 🧠 Work with smart and empathetic people who will help you grow and make a meaningful impact. - 🎉 Regular team offsite events to connect and collaborate. - 🌎 Fully remote team - choose where you live. - 🏝️ Unlimited vacation time - we strongly encourage all of our employees take at least 3 weeks per year. - 🛋️ Work from home stipend! We want you to have the resources you need to set up your home office. - 💻 Apple laptops provided for new employees. - 🧑‍🎓 Training and development budget for every employee, refreshed each year. - 👪 Maternity & Paternity leave for qualified employees. - 💵 Base salary: $80k–$120K USD, depending on knowledge, skills, experience, and interview results - 📈 Stock options - offered in addition to the base salary DIVERSITY, EQUITY, AND INCLUSION AT CONSTRUCTOR At Constructor.io http://Constructor.io we are committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, we welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group. Studies have shown that women and people of color may be less likely to apply for jobs unless they meet every one of the qualifications listed. Our primary interest is in finding the best candidate for the job. We encourage you to apply even if you don’t meet all of our listed qualifications. How we handle your data. By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, we will retain your data for a limited period as described in our Recruitment Privacy Notice https://constructor.com/recruitment-privacy-notice. After that period your data will be deleted or anonymized.

Location: Armenia; Spain; Serbia; Poland; Kyiv City; Kyiv; Odesa; North Macedonia 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