Вакансии

-6-8+ years building full-stack products, including time as the senior person on the work. -Strong relational database skills, Postgres or MySQL: schema design, indexing, query tuning, transactions, and what actually happens under load.

Location: Canada, Waterloo, ON Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. At PlayStation, we're passionate about both partnership and technology. We care about building performant products and powerful tools, solving practical problems in delightful ways. We love building the systems that millions of people use, and we're always our own first customers and critics. We strive to be at the forefront of development technologies and celebrate our diversity with engineering teams located on four continents from all walks of life. We build our teams to be places where every voice is heard, all input is valued, and our members have the creative freedom to make their ideas real. Sr Software Development Engineer in Test , ML Locations: Madison, WI and Waterloo, Ontario Canada P layStation is seeking a Sr Software Development Engineer in Test, ML to join our Madison team and lead quality efforts for machine learning-powered products and services. You will partner closely with ML engineers, software engineers, product managers, and data partners to build scalable test automation and validation approaches for systems serving PlayStation at global scale. This role is focused on building confidence in model reliability, production readiness, and behavioral consistency. Unlike traditional software backend testing, success here requires validating probabilistic systems through score distributions, cohort-level behavior, thresholds, and statistical signals, not just deterministic pass/fail assertions. Responsibilities: Drive architecture-level automation solutions and scalable quality engineering practices across multiple teams and product areas. Design, develop, and maintain automated test frameworks, test scripts for ML inference services and related ML platform components using python/Java Build and automate realistic, representative test account creations and datasets to evaluate model behavior across different scenarios Validate score distributions, ranking behavior, output quality, and other model signals for incoming ML models prior to release Perform different types of testing, including functional, integration, regression and API testing working with RESTful APIs, microservices and databases, and non-functional performance and load testing to verify inference service scalability, latency requirements and reliability under production like conditions Work closely with ML engineers and ops teams to ensure that all features and bug fixes come with automated test coverage, ensuring continuous integration and deployment Debug and analyze test failures, model anomalies and report defects Drive high standards of quality for both engineering decisions and customer-facing features with optimal test and automation strategy Develop and implement best practices for test automation that is highly scalable and maintainable Monitor test execution and optimize performance in test environments Come up with innovative solutions to organizational problems Required Qualifications: A Bachelor's degree in Computer Science or related field Minimum 5 years of experience as an SDET, with strong experience testing backend systems. Proficiency in Python/Java/Scala/go for test automation. Hands-on experience with API testing tools (e.g., Postman or similar) for distributed systems Familiarity with CI/CD pipelines and test execution in Jenkins or similar environments such as jenkins, GitHub Actions, ArgoCD, etc Hands-on experience building and maintaining automated test frameworks (e.g., pytest, JUnit, TestNG, etc.)Experience in working with databases  for backend validation. Experience with cloud and container technologies (AWS, GCP, Kubernetes, Docker, etc.) Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana, Datadog, etc.) for validating service health Strong understanding of software development lifecycle (SDLC) and agile methodologies. Excellent problem-solving skills and excellent communication skills. Preferred Qualifications: Experience testing machine learning systems, such as recommendation systems, search ranking systems, or other probabilistic ML application systems. Experience creating synthetic or seeded test data to simulate realistic customer/account behaviors. Experience evaluating model outputs using statistical techniques, distribution analysis, or scenario-based validation, rather than deterministic assertions. Knowledge of data pipelines, feature stores, inference systems or model-serving infrastructure (Seldon, KServe, Ray Serve, etc.) is a plus. Experience with performance and load testing tools Experience with Databricks or similar ML platform tooling Experience architecting and optimizing CI/CD pipelines, quality gates, and release validation systems using Jenkins or similar platforms. At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. The estimated Ontario base pay range for this role is listed below. $114,000 — $143,000 CAD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: United States, Madison, WI Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. At PlayStation, we're passionate about both partnership and technology. We care about building performant products and powerful tools, solving practical problems in delightful ways. We love building the systems that millions of people use, and we're always our own first customers and critics. We strive to be at the forefront of development technologies and celebrate our diversity with engineering teams located on four continents from all walks of life. We build our teams to be places where every voice is heard, all input is valued, and our members have the creative freedom to make their ideas real. Sr Software Development Engineer in Test , ML Locations: Madison, WI and Waterloo, Ontario Canada P layStation is seeking a Sr Software Development Engineer in Test, ML to join our Madison team and lead quality efforts for machine learning-powered products and services. You will partner closely with ML engineers, software engineers, product managers, and data partners to build scalable test automation and validation approaches for systems serving PlayStation at global scale. This role is focused on building confidence in model reliability, production readiness, and behavioral consistency. Unlike traditional software backend testing, success here requires validating probabilistic systems through score distributions, cohort-level behavior, thresholds, and statistical signals, not just deterministic pass/fail assertions. Responsibilities: Drive architecture-level automation solutions and scalable quality engineering practices across multiple teams and product areas. Design, develop, and maintain automated test frameworks, test scripts for ML inference services and related ML platform components using python/Java Build and automate realistic, representative test account creations and datasets to evaluate model behavior across different scenarios Validate score distributions, ranking behavior, output quality, and other model signals for incoming ML models prior to release Perform different types of testing, including functional, integration, regression and API testing working with RESTful APIs, microservices and databases, and non-functional performance and load testing to verify inference service scalability, latency requirements and reliability under production like conditions Work closely with ML engineers and ops teams to ensure that all features and bug fixes come with automated test coverage, ensuring continuous integration and deployment Debug and analyze test failures, model anomalies and report defects Drive high standards of quality for both engineering decisions and customer-facing features with optimal test and automation strategy Develop and implement best practices for test automation that is highly scalable and maintainable Monitor test execution and optimize performance in test environments Come up with innovative solutions to organizational problems Required Qualifications: A Bachelor's degree in Computer Science or related field Minimum 5 years of experience as an SDET, with strong experience testing backend systems. Proficiency in Python/Java/Scala/go for test automation. Hands-on experience with API testing tools (e.g., Postman or similar) for distributed systems Familiarity with CI/CD pipelines and test execution in Jenkins or similar environments such as jenkins, GitHub Actions, ArgoCD, etc Hands-on experience building and maintaining automated test frameworks (e.g., pytest, JUnit, TestNG, etc.)Experience in working with databases  for backend validation. Experience with cloud and container technologies (AWS, GCP, Kubernetes, Docker, etc.) Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana, Datadog, etc.) for validating service health Strong understanding of software development lifecycle (SDLC) and agile methodologies. Excellent problem-solving skills and excellent communication skills. Preferred Qualifications: Experience testing machine learning systems, such as recommendation systems, search ranking systems, or other probabilistic ML application systems. Experience creating synthetic or seeded test data to simulate realistic customer/account behaviors. Experience evaluating model outputs using statistical techniques, distribution analysis, or scenario-based validation, rather than deterministic assertions. Knowledge of data pipelines, feature stores, inference systems or model-serving infrastructure (Seldon, KServe, Ray Serve, etc.) is a plus. Experience with performance and load testing tools Experience with Databricks or similar ML platform tooling Experience architecting and optimizing CI/CD pipelines, quality gates, and release validation systems using Jenkins or similar platforms. At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. The estimated base pay range for this role is listed below. $152,000 — $190,000 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Workplace: remote Requirements 5+ years of hands-on data engineering experience, including delivery and support of production data solutions Practical experience with both Snowflake and Databricks Strong SQL and Python skills, including hands-on experience with PySpark Experience using dbt to develop, test, and maintain data transformations Experience designing and deploying cloud data solutions, preferably on Azure Strong data modelling skills, including dimensional modelling and an understanding of Data Vault 2.0 concepts Experience with Git, code reviews, automated testing, and CI/CD Ability to investigate data quality issues and troubleshoot pipeline failures and performance problems Experience working directly with clients or multiple stakeholder groups Ability to work effectively in agile and startup environments, adapting to changing priorities and evolving requirements Strong critical thinking and problem-solving skills, with the ability to challenge assumptions, evaluate alternatives, and make evidence-based decisions Ability to work independently, clarify ambiguous requirements, and take ownership of delivery Nice to have Experience with change data capture, streaming, or event-driven processing Experience with infrastructure as code, such as Terraform Experience with AWS in addition to Azure Snowflake or Databricks certifications Experience mentoring engineers or leading technical delivery Familiarity with BI tools such as Power BI, Tableau, or Looker Responsibilities Work with client stakeholders to understand business needs, assess existing systems, and define technical requirements Contribute to solution design, explaining technical options, trade-offs, and implementation effort Design, build, and maintain production ETL/ELT pipelines integrating databases, APIs, and file-based sources Develop data models and transformation workflows that support analytics and business processes Implement incremental loading, data validation, error handling, monitoring, and recovery mechanisms Improve pipeline performance and manage compute and storage costs Apply data security and governance practices, including access control and protection of sensitive data Use version control, code reviews, automated testing, and CI/CD to deliver maintainable solutions Create technical documentation and support knowledge transfer to client teams Support project estimation and planning, raise delivery risks, and mentor other engineers We offer Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota Competitive compensation that depends on your qualification and skills Career development system with clear skill qualifications Flexible working hours aligned to your schedule Options to work remotely Corporate medical insurance covering services of private and public medical centers English courses online Corporate parties and events for employees and their children Internal conferences, workshops and meetups for learning and experience sharing Gym membership compensation 5 days of paid sick leave per year with no obligation to submit a sick-leave certificate Any questions? Anna hh@itransition.com

Location: Москва Workplace: Гибрид, Удаленно Объединённая компания RWB (Wildberries & Russ) — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Обязанности Анализировать предметную область, выявлять зависимости между системами, определять границы сервисов и точки интеграции; Готовить системные спецификации: функциональные и нефункциональные требования (производительность, безопасность, масштабируемость, надёжность), критерии приёмки, User Stories и Use Cases; Проектировать API и контракты взаимодействий, поддерживать спецификации (OpenAPI/AsyncAPI), согласовывать интерфейсы с разработчиками и смежными командами; Создавать и поддерживать аналитические артефакты: C4-диаграммы, sequence-диаграммы, ER-модели; Участвовать в архитектурных обсуждениях, готовить ADR по своим решениям, проходить arch review; Согласовывать требования с разработкой, QA и эксплуатацией, обеспечивать единое понимание задачи в команде; Участвовать в груминге, планировании, оценке трудоёмкости, сопровождать задачи до приёмки и релиза; Помогать QA с критериями приёмки и тест-кейсами, проверять соответствие реализации требованиям; Поддерживать актуальность документации в Wiki и других системах хранения знаний; Принимать в зону ответственности закреплённые подсистемы и развивать экспертизу по ним. Требования Имеете опыт работы системным аналитиком от 2х лет в командах разработки распределённых систем; Умеете собирать требования у бизнеса, переводить их в формальные спецификации и доводить до разработки без потерь; Владеете практикой проектирования API (REST, gRPC, message brokers) и описания контрактов через OpenAPI/AsyncAPI; Понимаете, когда какой способ интеграции применять: синхронный/асинхронный, очереди, события - и какие у них следствия (SLA, консистентность, failure scenarios); Обладаете уверенным владением нотациями: UML, C4, sequence-диаграммы; Понимаете SQL и моделей данных, имеете представление о нормализации, денормализации и эволюции модели данных во времени; Умеете декомпозировать сложное на части системно: видеть границы между сервисами на уровне проектирования; Имеете опыт работы в Agile-командах, понимание ролей и процессов разработки; Готовность брать ответственность за свои подсистемы и развивать экспертизу по ним вдолгую; Опыт работы с AI-агентами и искусственным интеллектом; Будет плюсом - опыт работы с микросервисной архитектурой; владение BPMN; знакомство с практиками Domain-Driven Design; умение читать код, если потребуется разобраться в реализации. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: Hong Kong Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities. Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have Fun and Is Motion Gaming Back? , as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations. Nex employees may be expected to collaborate with colleagues across different time zones. Depending on the role and location, occasional travel may also be required for company events, external meetings, and other business-related activities. Important Notice: ⚠️ To protect yourself from recruitment scams, please note that Nex communicates through official company emails ending in @nex.inc . If you receive a suspicious email or message, please do not respond and report it immediately to us. Location: Hong Kong The Role As a Lead Software Engineer in Test (SDET) at Nex, you will build the test engineering capability within Platform Software Engineering that helps us ship reliable Nex Playground experiences. You will define and implement an automated test strategy spanning device software, cloud services, and hardware-integrated systems. From functional and regression testing to sustained device stress and cloud-scale load, you will design and build the tools, practices, and feedback loops that help engineers find problems early and release with confidence. You will lead through technical judgment, hands-on execution, and collaboration, and drive practical AI adoption in your work. Success Looks Like Teams can rely on fast, maintainable automated tests to detect meaningful regressions throughout development and release. Device software, hardware-integrated systems, and cloud services are tested under realistic conditions, including sustained stress and load at scale. Engineers can see test coverage, reliability trends, and release risks clearly, and use that information to improve products and test effectiveness. Test tools and methods continuously improve, and platform engineers can design and build more testable systems. What You'll Do Test strategy and technical leadership Own and evolve the automated test strategy for Platform Software Engineering. Prioritize coverage based on product risk and drive a high level of meaningful automated test coverage. Define test approaches, quality criteria, and release feedback with platform engineers working on device software and cloud services. Review and improve vendors’ test strategies and coverage, identify gaps, and work with vendors to address them. Research, evaluate, and develop new testing techniques and tools that improve coverage, speed, and confidence. Drive responsible, practical AI adoption in test design, automation, analysis, and engineering workflows, while maintaining human judgment and verification. Automation and test infrastructure Develop and operationalize functional, stress, and regression testing for software and hardware products, using both software-based and hardware-integrated techniques. Develop and operationalize cloud service load testing using synthetic workloads and data at scale; identify bottlenecks, failure modes, and regressions. Design, build, and evolve reusable test frameworks, harnesses, fixtures, and infrastructure that support reliable, repeatable testing across system boundaries. Keep automated testing integral to CI/CD, with actionable feedback that engineers can use throughout development and release. Improve product testability through better instrumentation, API design, separation of concerns, and effective use of mock objects and test data. Define and track metrics, reports, and dashboards that show test coverage, effectiveness, reliability, and trends over time. Engineering collaboration Partner with platform engineers to embed quality and testability into design and delivery rather than treating testing as a final gate. Work with platform engineers to investigate failures, identify root causes, and turn findings into product and test improvements. Raise the engineering bar through design reviews, technical guidance, mentoring, and sharing test practices, tools, and findings with platform engineers. Must Have Experience setting technical direction for test engineering across teams and independently delivering work from strategy through implementation and iteration. Strong hands-on software engineering skills, with experience designing, building, and maintaining automated test frameworks and infrastructure used by engineering teams. Experience defining and delivering test strategies for complex software systems, including functional, regression, and stress or reliability testing. Experience testing cloud services under load, including generating representative workloads and data and investigating performance or scalability bottlenecks. Experience integrating automated tests into CI/CD and improving the reliability, speed, and usefulness of test feedback. Strong understanding of software design for testability, including instrumentation, APIs, isolation, mocks, and test data. Experience applying AI tools to engineering and testing workflows, with a thoughtful approach to validation and data security. Nice to Have Experience testing connected consumer devices or systems that combine device software, firmware, hardware, and cloud services. Experience with hardware-in-the-loop testing, automated test fixtures, or validation of cameras, sensors, or other hardware modules. Familiarity with Android/AOSP, embedded systems, or device-level debugging. Experience with soak, endurance, fault-injection, or other reliability testing techniques. Experience building products for children and families. We Offer Competitive compensation package. Flexible working hours and vacation policy. Product-driven culture that treasures talents and individual growth. Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field Nex is located in San Jose, California, USA and Hong Kong . Learn more about us at https://www.nexplayground.com/about . We encourage applications even if you don’t meet more than 50% of the requirements — we believe that experience comes in many forms! Nex is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, gender identity, and sexual orientation), national origin, ancestry, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable law. This policy applies to all individuals at every stage of the employment relationship, including all current and prospective employees, and covers all employment decisions including recruitment, hiring, job assignment, promotion, compensation, benefits, training, discipline and termination. We are committed to providing a workplace free from discrimination and harassment and to fostering an inclusive environment for all employees.

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT RAMP LABS Ramp Labs is Ramp's AI research group. We post-train models on finance work, build the environments and evals they learn from, and ship them into the products 70,000+ companies use to move money. We think the defensible part of an AI product is the loop: proprietary environments, evals that track what customers care about, and models trained against both. ABOUT THE ROLE Every model decision at Ramp Labs runs through a benchmark. Which model to route to. Whether a post-training run worked. Whether an agent is ready to post a corrective journal entry to a customer's ledger without a human in the loop. You'll own Ramp's benchmarking effort. You'll turn real finance work (expense policy review, invoice coding, month-end close, spreadsheet Q&A) into benchmarks and RL environments, build the graders, and tell the team what is actually improving. WHAT YOU’LL DO - Build benchmarks from real workflows: turn agent traces, customer edge cases and expert labels into versioned benchmark suites for long-horizon agents that run for hours across tools and systems, plus tool use, multi-step reasoning and financial judgment - Own the benchmarking effort: decide what we measure, run benchmark suites across models and agent versions, and report results the team makes decisions on - Design graders: rubrics, programmatic checks and llm-as-judge, calibrated against human agreement, with a clear view of where each one breaks - Close the loop with post-training: turn evals into reward functions and RL environments, and catch reward hacking and contamination before they ship - Publish external benchmarks and write-ups on how models perform at real finance work WHAT YOU NEED - Hands-on experience building LLM benchmarks or evals that people used to make real decisions - Strong Python and software engineering; your eval code is reproducible and fast - Statistical judgment: variance, confidence intervals, sample size, inter-rater agreement, and knowing when a delta is noise - Working knowledge of post-training (SFT, RL with verifiable rewards) and how evals feed into it - Comfort with SQL and large datasets; you'll work directly from production traces - Taste for what is worth measuring NICE-TO-HAVE - Industry experience on an eval, benchmarking or post-training team - Built RL environments or verifiers for agentic tasks - Published benchmark or evaluation research (NeurIPS Datasets & Benchmarks, ICML, ACL) - Built or contributed to eval frameworks (verifiers, lm-evaluation-harness, HELM or similar) - Familiarity with accounting, finance or other expert-domain work - Public work we can look at: a benchmark, an eval suite, a failure-analysis write-up BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: Australia; Hong Kong The Global Security Operations Engineer is a senior technical role responsible for strengthening the firm's ability to detect, investigate, respond to, and recover from cybersecurity threats across its global environment. Working as part of the Global Security operations team, the successful candidate will lead complex security investigations, develop and optimize threat detection capabilities, and implement automation to improve the effectiveness and efficiency of security operations. The role combines hands-on incident response with proactive security engineering, helping the firm move from reactive alert management toward an increasingly automated, intelligence-led security operating model. Operating within a geographically distributed organization, the successful candidate will work closely with global and regional technology and security teams, provide technical leadership during significant incidents, and contribute to the continuous improvement of security capabilities. Based in APAC, the role will provide critical regional coverage as part of a coordinated global security operations function. Responsibilities Advanced Investigation and Incident Response Serve as a senior escalation point for security incidents, assuming ownership of complex, ambiguous, and high-severity events that exceed first-line triage capability. Conduct detailed investigations to establish initial access vectors, attacker activities and objectives, scope of compromise, and dwell time, documenting findings to an appropriate evidentiary standard. Perform root cause analysis on confirmed incidents and near misses, identifying underlying security control or process failures rather than simply addressing immediate symptoms. Act as technical lead during significant incidents across containment, eradication, and recovery, coordinating activities across global and regional teams. Conduct host and network forensics and malware triage, engaging specialist forensic providers where investigations exceed internal capabilities. Translate technical findings into clear statements of business risk for leadership and drive post-incident corrective actions through to closure. Investigate identity-based attacks, account compromise, unauthorized access, and suspicious activity across cloud platforms and enterprise applications, including Microsoft 365 and Entra ID. Participate in an on-call rotation and provide out-of-hours escalation support during serious incidents, and undertake additional projects and activities as required. Security Automation & Detection Engineering Design, build, tune, and maintain detection content mapped to the MITRE ATT&CK framework, identifying and reporting coverage gaps against threat actor tactics, techniques, and procedures relevant to the firm. Reduce false-positive volume and analyst alert fatigue through systematic tuning, suppression logic, and automated alert enrichment. Operationalize threat intelligence by translating reporting and indicators of compromise into actionable detections, threat-hunting hypotheses, and preventive controls. Design, develop, and maintain automated response playbooks, including Logic Apps, automation rules, and SOAR workflows, to reduce mean time to detect and respond. Write and maintain queries, scripts, and code (KQL, PowerShell, Python, or equivalent) supporting detection, enrichment, automated triage, and reporting. Integrate security tooling and data sources through APIs and connectors, including onboarding new log sources with appropriate parsing, normalization, and validation. Continuously assess and improve security monitoring, detection, and response capabilities, identifying opportunities to strengthen coverage, reduce operational complexity, and improve effectiveness through automation and technology optimization. Identify opportunities to apply AI-assisted investigation, alert triage, and security automation capabilities to improve detection effectiveness, reduce manual effort, and accelerate incident response. Conduct proactive threat hunting to identify suspicious activity, validate detection effectiveness, and uncover threats that may evade existing security controls. Requirements This is a hands-on technical role requiring demonstrated depth in security operations, sound technical judgment, and the ability to work with limited supervision. We value analytical rigor, curiosity, initiative, and a willingness to share knowledge and develop others as highly as any individual technology or certification. Compulsory Requirements: 5+ years of experience in security operations, incident response, or detection engineering, including demonstrable experience working in a Security Operations Center (SOC). Proven experience leading complex technical investigations end to end, from escalation through root cause analysis to documented remediation. Deep, hands-on expertise with modern SIEM and endpoint detection and response (EDR) platforms, including authoring and optimizing detection logic, conducting investigations, and managing data onboarding. Strong practical automation and scripting abilities, including KQL and at least one of PowerShell or Python, with demonstrated experience developing and operating automation in production. Experience designing and maintaining automated response playbooks using Logic Apps, automation rules, or comparable SOAR capabilities. Advanced knowledge of the MITRE ATT&CK framework, Cyber Kill Chain analysis, and established incident response methodologies, such as PICERL. Strong working knowledge of TCP/IP and networking, Windows and Linux operating systems, Active Directory, Entra cloud identity and authentication, and security operations across cloud and hybrid environments. Experience with host and network forensics, malware triage, and the analysis of malicious email headers, links, and attachments. Strong written and verbal communication skills, including the ability to communicate technical findings and security risks to both technical and nontechnical audiences. Ability to work independently and collaborate effectively with geographically dispersed teams, with flexibility to provide out-of-hours support during significant security incidents. Occasional travel to support global cybersecurity operations and incident response may be required Preferred Requirements: Bachelor's degree in Information Security, Information Technology, Computer Science, or a related discipline, or equivalent professional experience. Industry certifications such as GCIH, GCIA, GCFA, GREM, OSCP, CySA+, SC-200, AZ-500, or CISSP. Hands-on experience with Microsoft security technologies, including Microsoft Sentinel, Microsoft Defender XDR, Microsoft Entra ID, and Microsoft Azure. Experience operating within a global, federated, or shared-services model, ideally in professional services or another highly regulated sector. Familiarity with ISO 27001, the NIST Cybersecurity Framework, or comparable control frameworks Benefits Benefits are aligned with the respective location. The above is intended to describe the general content of and requirements for the performance of this job.  It is not to be construed as an exhaustive statement of essential functions, responsibilities or requirements.

Location: United States, San Mateo, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. How would you like to influence the most successful gaming platform of all time by validating the very technology our developers use to build extraordinary games? Role Summary: Sony Interactive Entertainment (PlayStation) is seeking a Software Development Engineer in Test (SDET) to provide hands-on engineering excellence across our SDK ecosystem. This role focuses on building tools and infrastructure for analyzing the quality of the PlayStation SDK and related technology. While this is the primary domain, there are opportunities to expand into 3D graphics, spatial audio, artificial intelligence, and the fully integrated PlayStation game development environment. We are looking for someone who wants to directly impact PlayStation technology by developing tools and automated systems that deeply analyze SDK components and platform capabilities. You will help strengthen and evolve our testing infrastructure, raise the technical bar for quality engineering, and drive confidence in every release. If you’re passionate about solving complex system-level challenges and influencing technology used by people around the world, we want to hear from you, come help us power the future of PlayStation! What You'll Be Doing: - Crafting and improving automated test cases, sample applications, testing tools and frameworks for SDK libraries and low level software (C++) - Build and improve test infrastructure using Python and Groovy to simplify building, testing, and demonstrating PlayStation® sample applications. - Monitor test infrastructure, analyze automation failures, improve reliability, and optimize runtime performance. - Partner closely with software production and offshore teams to compose robust test plans, validation strategies, and documentation. - Define scalable, maintainable automation and infrastructure patterns that support long-term platform growth. - Contribute to architectural discussions for test systems spanning C++, embedded firmware (C), and Python-based infrastructure. - Identify technical risks early and drive mitigation strategies to protect release confidence. What We're Looking For: - Excellent interpersonal skills, including presenting technical direction to both engineers and executives. - Strong programming proficiency in Python & C++, as well as PyTest and GTest frameworks. - Self-directed, committed to producing strong results, and passionate about high-quality automation. - Experience building, running, and debugging software in both Windows & Linux environments. - Deep understanding of automation trade-offs and architectural design decisions. Nice-to-Have: - Exposure to Android platform, including SDK, tooling and architecture - Prior experience with PlayStation SDK development is helpful. - Familiarity with Unity, Unreal Engine or other game engine technology. - Containerization technology such as Docker and Kubernetes. - Proven experience leading complex debugging efforts. At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment. Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: Chicago, New York City, London 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 Unified Platform (UP) organization builds and offers managed data services, shared infrastructure, and metadata management systems that underpin our common query layer and power batch and streaming pipelines that power teams across DRW. As a Staff Engineer focused on the Data Platform , you will bring experience scaling data platforms to shape and uphold DRW’s long-term data platform vision, translating data strategy into engineering products and managed services used by Traders, Quantitative Researchers, and Back-Office personnel. You will work across tooling, producer, and consumer teams to connect business priorities with technical direction and hands-on delivery. This is an individual contributor role combining strategic thinking, architectural leadership, software development, and mentorship, with responsibility for technical initiatives that span teams. Key Responsibilities Contribute to and uphold the overall vision for DRW’s Data Platform, guiding its architecture and evolution to advance the firm’s data strategy. Work with engineering leads , product managers , and stakeholders to understand business priorities and translate data strategy into engineering products, platform capabilities, and contribute to the sequenced technical roadmap. Design , guide, and build managed platform services for data storage, distributed query execution, batch and streaming processing, and workload orchestration, owning their lifecycle from provisioning and upgrades through production support , refining the managed offers from direct user feedback. Lead delivery across team boundaries, turning ambiguous requirements into practical designs and measurable outcomes while balancing immediate business needs with long-term platform goals. Drive architecture reviews and technology decisions, documenting trade-offs and building consensus around patterns that uphold the platform vision across the data ecosystem. Own the strategy for large-scale data governance and b uild platform capabilities for metadata management, discovery, lineage, schema evolution, and data qua lity. Partner with security and data stakeholders on access controls and usage tra cking. Troubleshoot distributed systems, contribute to production support and incident response, and drive lasting improvements through root-cause analysis, automation, and operational documentation. Mentor engineers across teams through design reviews, code reviews, and hands-on collaboration, strengthening engineering practices and helping others take ownership of complex systems. Required Qualifications Extensive experience designing, building, and operating large-scale, data-intensive distributed systems , including technical leadership of initiatives spanning multiple teams. Experience coordinating with engineering leadership to accelerate team- and organization-wide delivery within existing processes and practices. A track record of translating data or business strategy into engineering products and technical roadmaps, with the judgment to align near-term delivery with a long-term platform vision. A track record of building and offering managed platform services to other teams, owning provisioning, upgrades, reliability, and ongoing support throughout the service lifecycle , working directly with users to incorporate their requirements and feedback into product and roadmap decisions. Experience scaling metadata management systems, such as data catalogs or lineage tracking systems , that span multiple storage and compute technologies . Expertise in Java and/ or Python, with a proven ability to deliver high-quality, maintainable production software, and strong familiarity with SQL and Linux environments. Deep understanding of distributed systems trade-offs, including consistency, fault tolerance, concurrency, partitioning, and recovery from partial failures. Production e xperience building both batch and stream processing systems at scale , demonstrating practical knowledge of state management, replay, backpressure, and delivery guarantees. Experience deploying data processing and query technologies such as Flink, Spark, and ClickHouse , and the ability to guide platform us ers on the performance and operational trade-offs among them. Experience with multiple storage technologies, including object storage, relational or NoSQL databases, and event streaming systems such as Kafka . In-depth k nowl ed ge of the internals of open table formats such as Iceberg or Delta Lake. Extensive e xperience with data modeling, schema governance and evolution, and formats and serialization systems such as Arrow, Parquet, Protobuf , or Avro. Familiarity with building and operating cloud-native data infrastructure, including Kubernetes, workflow orchestration, automated deployment, and production monitoring. Excellent written and verbal communication skills, with the ability to communicate a technical vision, explain trade-offs, align stakeholders, and lead through influence across a globally distributed organization. Strong technical judgment and problem-solving skills, with the ability to prioritize competing needs, deliver incrementally in a fast-paced trading environment, and develop other engineers. Desirable Qualifications Experience scaling managed data services across teams with different workloads, service expectations, and operational constraints. Hands-on knowledge of data governance in regulated environments. Familiarity with financial datasets and the needs of trading, quantitative research, or back-office users. The annual base salary range for this position is $200,000 to $275,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts. 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-Insert Recruiter Initials Here1]

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai