Вакансии

Задачи: — Управление командой из 8 человек по модели 1:1 — Выстраивание рабочих процессов внутри команды — Набор персонала — Оценка эффективности сотрудников (перформенс-менеджмент) — Ведение бэклога задач командой — Решение конфликтов между членами команды — Ревью кода разработчиков — Принятие архитектурных решений Требования: — Опыт от 7 лет в области данных — От 3-х лет опыта реального управления командой более 5 человек (включая найм, оценку производительности, увольнения)

Задачи: — Развивать платформу данных; — Разрабатывать и оптимизировать ETL/ELT-процессы; — Работать с batch- и streaming-пайплайнами; — Развивать модели данных и хранилище.

Задачи: — Развивать Data Lakehouse и единое DWH — Строить ETL/ELT-пайплайны от источника до готовой витрины — Разрабатывать интеграции с внутренними и внешними системами — Настраивать оркестрацию и регулярное обновление данных — Работать с data quality и metadata management — Участвовать в выборе инструментов и архитектурных решений — Взаимодействовать с архитекторами, DevOps, ИБ и бизнесом Требования: — Уверенный Python и SQL — Опыт работы с Airflow или Dagster — Опыт построения ETL/ELT-пайплайнов — Хорошее понимание DWH и современных подходов к работе с данными — Опыт работы с Kafka / streaming — Опыт в небольших командах или проектах, где приходилось самостоятельно закрывать широкий круг задач Будет плюсом: Trino, Apache Iceberg, DBT, ClickHouse Условия: — Оформление по ТК РФ — ДМС со стоматологией после испытательного срока — Компенсация питания — Полугодовые премии — Ежегодная индексация — Спортивные активности и корпоративные мероприятия — На испытательном сроке — офис 5/2, далее возможен гибрид по договорённости

Задачи: — поддерживать и настраивать IP-телефонию на базе Asterisk: dialplan, SIP-транки, маршрутизация; — подключать SIP-провайдеров, обеспечивать входящую и исходящую связь, автообзвон; — интегрировать Asterisk с backend и CRM через ARI/AMI; — диагностировать проблемы SIP/RTP и качества связи; — работать с CDR и записями звонков; — использовать Asterisk Realtime и MariaDB/MySQL; — заниматься безопасностью инфраструктуры: firewall, Fail2Ban, ACL; — контролировать мониторинг и самостоятельно разбираться с возникающими инцидентами. Требования: — опыт работы с Asterisk от 2 лет; — глубокое понимание SIP, VoIP, NAT, RTP и кодеков; — практический опыт с ARI и/или AMI; — уверенное администрирование Linux; — базовые навыки работы с MariaDB/MySQL; — понимание безопасности VoIP и умение самостоятельно решать технические проблемы. Плюсом будет опыт с Redis, Nginx и системами мониторинга. Условия: — зарплата $3000 в месяц, фиксированно; — полностью удалённый формат работы / Home Office; — график Пн–Пт, 09:00–18:00 (UTC+3); — обед 1 час + три перерыва по 15 минут; — работа в формате онлайн-офиса с включённой камерой и демонстрацией экрана в рабочее время. Работа в Армении.

Задачи: — Определять техническую архитектуру и направление для AI-систем на платформе, включая model serving, функции на базе LLM, агентские рабочие процессы и инфраструктуру. — Проектировать производственные системы, которые надежны и наблюдаемы, несмотря на недетерминированное поведение AI. — Архитектурно разрабатывать фреймворки оценки, защитные механизмы и системы качества для безопасного, измеримого и надежного поведения AI в масштабе предприятия. — Руководить сложными задачами AI-инжиниринга от прототипирования до промышленного внедрения. — Устанавливать инженерные стандарты и лучшие практики для AI-нативных систем, а также наставлять старших инженеров. — Оценивать и интегрировать новейшие достижения в foundation models, агентских фреймворках и современном AI-стеке. Требования: — Обширный опыт построения и архитектуры производственных программных систем в масштабе, с глубокими знаниями AI/ML систем. — Практический опыт создания производственных систем на базе LLM или моделей машинного обучения, включая проектирование для недетерминированного поведения. — Глубокие знания распределенных систем, model serving и инфраструктуры для надежного запуска AI в масштабе. — Опыт проектирования фреймворков оценки, защитных механизмов и систем качества для AI-функций. — Опыт установления технической архитектуры и стандартов, а также наставничества старших инженеров. — Уверенное владение одним из основных языков программирования (Python, Java или Go) и современными облачными инфраструктурами (AWS или GCP). — Практический опыт с агентскими системами, проектирование и укрепление AI-агентов, принимающих автономные решения в продакшене. — Знания современного AI-стека: retrieval-augmented generation, vector stores, агентские фреймворки, оркестрация моделей, инструменты оценки. Условия: — Удаленная работа из США. — Полная занятость. — Годовой базовый оклад от $165,000 до $185,000 USD. — Возможны бонусы по результатам работы, социальные гарантии и другие планы стимулирования. Компания Salesloft строит систему Predictive Revenue System, объединяющую AI и реальные данные для поддержки решений в области доходов предприятий. Роль Principal Software Engineer, AI — самая старшая индивидуальная инженерная позиция в AI-команде, ответственная за архитектуру и стандарты AI-систем, которые используют тысячи команд по всему миру.

Micron Technology приглашает на работу выпускников на позицию инженерного техника. Вакансия открыта как для новичков, так и для специалистов с опытом работы. Кандидаты должны иметь соответствующее техническое образование и готовы развиваться в области инженерных технологий. Работа предполагает выполнение технических задач, связанных с производственными процессами и поддержкой оборудования. Компания ценит инициативность и стремление к профессиональному росту.

Задачи: — Проектировать и развивать LLM/AI-системы и AI-агентов; — Строить многошаговые agentic workflows: routing, tool calling, state management; — Работать с Prompt Engineering и Context Engineering; — Управлять контекстом, памятью и состоянием диалога; — Реализовывать structured output и function/tool calling; — Строить RAG-пайплайны: chunking, embeddings, retrieval, reranking; — Интегрировать LLM с API, базами данных и внутренними сервисами; — Разрабатывать backend-сервисы на Go и AI-компоненты на Python; — Исследовать новые модели и подходы, быстро проверять гипотезы; — Выбирать модели с учетом качества, latency и стоимости; — Строить систему оценки качества: evals, regression tests, golden datasets, LLM-as-a-judge; — Анализировать логи и traces, улучшать качество и стабильность агентов; — Реализовывать retries, fallbacks, timeouts и обработку ошибок; — Доводить решения от прототипа до стабильного production. Требования: Go: — production-сервисы; — REST/gRPC API; — concurrency; — работа с БД; — микросервисная архитектура. Python: — AI-сервисы и backend; — async; — API; — обработка данных; — быстрое прототипирование. Также важно: — Практический опыт разработки production-приложений с LLM; — Понимание архитектуры современных AI-систем: prompt → context → tools → agent → state → evaluation; — Опыт Prompt Engineering: system prompts, few-shot, prompt chaining, structured output; — Опыт Context Engineering: memory, summarization, динамическая сборка контекста; — Опыт создания AI-агентов и agentic workflows; — Работа с LLM API и понимание особенностей разных моделей; — Понимание RAG: embeddings, vector search, retrieval, reranking, hybrid search; — Понимание evals, regression testing и анализа production-логов; — Опыт с PostgreSQL, MongoDB, Redis, Docker, Git, CI/CD; — Понимание production-подходов: observability, scalability, latency, retries, fallbacks и контроль стоимости.

Задачи: — Администрировать, поддерживать и оптимизировать облачные платформы и инфраструктуру. — Настраивать, конфигурировать, поддерживать и совершенствовать CI/CD- и GitOps-пайплайны. — Внедрять и поддерживать практики Infrastructure as Code (IaC) с использованием таких инструментов, как Ansible, Terraform, Pulumi. — Управлять контейнеризированными средами с использованием Docker, Docker Swarm и Kubernetes. — Обеспечивать надежную инфраструктуру и процессы развертывания для BI-сервисов и приложений, включая ClickHouse, SQL, Airflow, Tableau, Superset. — Тесно взаимодействовать с командами разработки и BI для повышения надежности развертывания, консистентности окружений и операционной эффективности. Требования: — Уверенные навыки настройки и администрирования как минимум одной облачной платформы. — Уверенные навыки построения и автоматизации процессов развертывания с использованием CI/CD и GitOps. — Опыт построения масштабируемой IT-инфраструктуры. — Уверенные навыки настройки Linux-серверов и устранения неисправностей. — Практический опыт работы с Kubernetes. — Понимание сетевой модели OSI. — Понимание принципов построения надежной и масштабируемой инфраструктуры, а также автоматизированных процессов развертывания. — Знание принципов информационной безопасности и защиты данных. — Интерес к работе с инфраструктурой для Business Intelligence и data-related решений либо соответствующий практический опыт.

Location: Bengaluru, India We are looking for a Senior AI Quality Engineer (L3+) with strong Python skills and hands-on experience testing AI and ML systems. You will analyze requirements, define evaluation strategies, build automated checks, validate models and outputs, test APIs and data pipelines, verify defects through resolution, and contribute to release readiness for your assigned product area. You will both evaluate AI systems and design, build, and operate QE agents for the software testing life cycle. AI-assisted work is part of everyday QE practice on this engagement, with Ragas, DeepEval, promptfoo, Cursor, and other AI and ML tools used for evaluation, scripting, test generation, analysis, and maintenance. You will also build golden datasets, calibrate LLM-as-judge methods, test agentic workflows and RAG systems, implement self-healing and intelligent test approaches, and diagnose quality, safety, performance, and cost failures across distributed AI services. This position is intended for Hyderabad-based candidates who are available to work from the office. The client delivers AI agents, retrieval systems, models, and data pipelines across a broad technology landscape. Quality is part of delivery through evaluation gates, automated regression, model and data validation, security and privacy checks, and evidence that a release is ready. Python is the primary language for AI evaluation, model validation, API testing, data-pipeline quality, and QE agent design. The role combines deterministic automation with probabilistic evaluation to assess AI behavior across models, data, retrieval, tools, and integration surfaces, and designs and operates agents that support the software testing life cycle. Analyze requirements and define evaluation strategies, golden datasets, regression benchmarks, baselines, thresholds, and blocking metrics with domain-expert review. Design, build, and orchestrate AI agents, persona-based workflows, and reusable skills for the STLC using LangChain, LangGraph, or Semantic Kernel. Build automated evaluations and benchmarks for LLMs, agents, RAG, retrieval, tool trajectories, and contracts using Ragas, DeepEval, and promptfoo. Calibrate LLM-as-judge against human grading, manage non-determinism through repeated runs, and detect regression and drift. Write and maintain Python and pytest automation for AI, ML, API, data, and integration workflows. Use Cursor and agentic tools to analyze requirements, generate and review tests, and maintain evaluation suites. Validate deep-learning, reinforcement-learning, and predictive models using LangChain. Build AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, and defect triage, plus intelligent test selection and defect prediction. Integrate LLMs, APIs, embeddings, vector databases, retrieval pipelines, and enterprise data sources into QE frameworks using Python. Test REST and streaming APIs, agent tools, and RAG and MCP integrations, including authentication, errors, retries, and recovery. Test data and retrieval pipelines for schema integrity, lineage, idempotency, fallback, promotion gates, and rollback. Integrate evaluations, benchmarks, and agent workflows into QE frameworks and CI/CD on GitHub Actions, review Allure results, and keep TestRail aligned with automated coverage. Test prompt injection, jailbreaks, unsafe tool use, data exfiltration, access control, PII controls, and guardrails. Measure accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnose distributed failures. Implement tracing, monitoring, and production evaluation using Langfuse, LangSmith, or a comparable AI observability platform. Deploy and operate QE agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Deploy and validate AI and ML solutions on AWS or Azure, including secrets, database services, and cloud-native test infrastructure. Report, track, and verify defects in Jira, and work with domain experts, product, and engineering on traceable coverage and untested scope. Contribute to release readiness for the assigned product area without owning sign-off for the whole team. Strong Python proficiency with pytest for test and evaluation automation. Hands-on experience building and orchestrating AI agents and agentic workflows with LangChain, LangGraph, Semantic Kernel, or comparable frameworks. Hands-on experience building AI evaluations and benchmarks with Ragas, DeepEval, promptfoo, or equivalent tools. Hands-on use of Cursor or agentic development tools for test generation and maintenance. Experience defining AI evaluation strategies, golden datasets, regression benchmarks, and LLM-as-judge calibration, including non-determinism and drift. Experience validating deep-learning, reinforcement-learning, and predictive models with LangChain. Experience building AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, defect triage, intelligent test selection, and defect prediction. Experience integrating LLMs, APIs, embeddings, vector databases, and retrieval pipelines with automation frameworks in Python. Experience testing REST and streaming APIs, agent tools, RAG workflows, and MCP integrations. Experience testing data and retrieval pipelines for correctness, lineage, and safe promotion. Experience integrating AI evaluation and agent workflows with automation frameworks and CI/CD using GitHub Actions, Allure, TestRail, or equivalent tools. Experience testing AI security, privacy, access control, and guardrails. Experience measuring accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnosing distributed failures. Experience with AI tracing, monitoring, and production evaluation using Langfuse, LangSmith, or comparable tools. Familiarity with AWS, Azure, or GCP cloud AI services and agent platforms for deploying and validating AI and ML solutions. Experience deploying and operating agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Experience managing defects through resolution in Jira or an equivalent tool. Ability to work with domain experts, product, and engineering on ground truth and coverage gaps. Experience contributing to release readiness for an assigned product area. Playwright and TypeScript experience for UI and API automation. Real-device and cross-browser execution using LambdaTest, Sauce Labs, or an equivalent platform. Knowledge of AI-driven test optimization and coverage analytics using SeaLights or an equivalent platform. Experience contributing reusable components to shared automation frameworks and reviewing automation code through Git and GitHub workflows. Experience managing test data and troubleshooting test environments needed for reliable execution. Experience triaging security findings and validating operational readiness, monitoring, rollback, and recovery controls. Experience producing audit-ready evaluation reports and improving shared evaluation patterns, automation libraries, and datasets.

Location: Bengaluru, India We are looking for an experienced Platform Engineer to support the existing AWS infrastructure and contribute to its ongoing modernization and consolidation. The role combines hands-on cloud engineering with a product mindset. We are not looking only for someone who can build or operate infrastructure – we need an engineer who treats the platform as a product and product engineering teams as customers: starting from developer friction rather than a preferred tool, building self-service and golden paths, and proving impact with data. The working schedule is aligned with UK business hours, from 9:00 AM to 5:30 PM UK time. Participation in an on-call rota may be required. This position includes a sign-in bonus. You will contribute to a transformation from team operated infrastructure toward a platform with clear self service capabilities. The platform is run as an internal product: product engineering teams are its customers, and success is measured by developer experience, adoption, reliability, delivery speed and cost. Design and evolve reliable AWS, Kubernetes, Terraform and CI/CD platform capabilities for engineering teams. Understand developer friction through conversations, feedback and data before choosing a technical solution. Build self-service capabilities, golden paths and guardrails that make the right thing easy for engineers. Measure outcomes (e.g. lead time, deployment frequency, reliability, cost) to prioritize work and confirm improvements. Work with product engineering teams to iterate on solutions and drive platform adoption. Balance standardization and controls with usability, so the platform enables teams rather than adding process. Strengthen observability, resilience, security and incident readiness. Take part in incident response, post-incident reviews and the on-call rota. Strong hands on AWS platform engineering experience in production environments. Practical experience with Kubernetes, EKS, Docker, Terraform, and Helm. Strong CI/CD experience, particularly with GitHub Actions and reusable pipeline design. Experience with logging and monitoring, networking and protocols, secrets management, and service discovery. Experience with at least one programming or scripting language. Evidence of designing secure, reliable, and maintainable platform solutions. Experience supporting business critical services, including participation in an on call rota. Experience identifying developer needs and translating them into practical platform improvements. Experience building self service capabilities, reusable workflows, or guardrails adopted by engineering teams. Ability to use data and metrics to prioritize platform improvements and evaluate their impact on developer experience, reliability, delivery speed, or cost. Experience collaborating with engineering teams to gather feedback, iterate on solutions, and drive adoption. Ability to balance standardization, security, and operational controls with developer usability. Experience with Honeycomb, OpenTelemetry, Prometheus or Splunk, including SLO-driven practices. Experience using platform engineering metrics to measure adoption, developer experience and operational outcomes. Pragmatic use of AI-assisted engineering tools to improve quality and productivity.

Location: Ukraine; Kazakhstan; Uzbekistan; Georgia; Armenia Crystalia Glass LLC is a leading supplier of premium glass and aluminum structures for residential and commercial projects across The USA. Our clients include SpaceX, Nike, Starbucks, Bank of America, Yves Saint Laurent, and many others. The company was founded in 2017 in New York and is rapidly growing. We have offices in Los Angeles, Florida, and Texas, and our projects are implemented throughout the United States. We are looking for a Design Engineer CAD for Interior glass partitions / Glass production English - B1+; Russian/Ukrainian language - C1+ Key Responsibilities: Developing custom mechanical assemblies (Autocad 2D) Preparing standard technical drawings and specifications for Shower Doors, Railings, Mirrors , SPEC SHEETS Catalog drawings and models Working with measurements, sketches, and diagrams (Imperial system) Requirements Experienced AutoCAD 2D Experience with measurements, sketches, and diagrams Experience with all-glass structures (partitions, railings, interior doors, swing doors, sliding systems, windbreaks) Ability to analyze and optimize design solutions in terms of cost, time, and resources Ability to work independently and make technical decisions Ability to work with project documentation at various stages of development, including sketches, working drawings, and specifications English language- B1+; Russian/Ukrainian language - C1+ Benefits Full-time, remote work 8-hour workday with flexible working hours ( based in NY time zone) Contract - format: B2B (individual entrepreneur or Corporate Entity) Paid Vacation: For the first year - 1 week ; for the second year and after - 2 weeks Engaging projects designed in the Imperial (US) system In-house training and mentorship programs Performance-based pay model (piecework) after the probation period.

Location: Ukraine; Kazakhstan; Georgia; Armenia; Poland Crystalia Glass LLC is a leading supplier of premium glass and aluminum structures for residential and commercial projects across The USA. Our clients include SpaceX, Nike, Starbucks, Bank of America, Yves Saint Laurent, and many others. The company was founded in 2017 in New York and is rapidly growing. We have offices in Los Angeles, Florida, and Texas, and our projects are implemented throughout the United States. We are looking for a CAD Engineers Team Lead for Interior glass partitions / Glass production with Russian (C1+) and English (B2+) Employment Type: - Full-time 8.30 am - 5 pm, NY (EST zone). - B2B contract (sole proprietorship status or corporate entity). - Remote work. Key Responsibilities: Operational Management: Daily review of active projects in ClickUp, distributing workloads, monitoring task execution, and tracking strict project deadlines. Quality Assurance & Review: Controlling drawing quality, checking project documentation, and ensuring compliance before final delivery. Technical Mentorship: Mentoring project engineers on complex/non-standard projects, participating in node development, and leading daily/weekly team syncs. Process Standardization: Customizing and maintaining workspaces in Slack/ClickUp, building internal document templates, and updating working groups. Reporting: Preparing weekly and monthly progress and efficiency reports of the department for the Head of Engineering. Working with measurements, sketches, and diagrams (Imperial system) USA. Requirements English language- B2+ ; Russian/Ukrainian language - C1+ Confident proficiency in core engineering software AutoCAD 2D, Solidworks 3D or similar. Higher technical education (Civil Engineering, Mechanical Engineering, or a relevant engineering degree). Solid experience and a deep understanding of core engineering design principles. 2–3+ years of experience working as a project engineer with a proven track record of successfully executing complex engineering projects. Strong background working with task management systems and time-tracking software. Experience in developing internal onboarding/training materials (video instructions, checklists, technical templates) to optimize department workflows. Ability to analyze and optimize design solutions in terms of cost, time, and resources. Ability to work independently, make firm technical decisions, and proactively resolve operational bottlenecks. Ability to work with project documentation at various stages of development, including sketches, working drawings, and final specifications. Proven experience interacting with cross-functional departments (Project Managers, Estimators, CQO, Suppliers, Manufacturing, and Site Installation teams) to clarify technical requirements and resolve conflicts. Benefits Full-time, remote work. 8-hour workday without flexability 8:30am - 5:00pm EST ( NY time zone). Collaboration format: B2B (individual entrepreneur or Corporate Entity). Paid Vacation: For the first year - 5 days; for the second year and after - 10 days. Engaging projects designed in the Imperial (US) system. In-house training and mentorship programs.

Location: Dallas, TX; Des Moines, IA; Fort Worth, TX; Lincoln, NE; North Kansas City, MO; Oklahoma City, OK; Olathe, KS; Omaha, NE; Overland Park, KS; Springfield, MO; Tulsa, OK Company Description We are Olsson. We engineer and design solutions that improve the world around us. As a company, we promise to always be responsive, transparent, and focused on results – for our people, our clients, and our company. We’re a people-centric firm, so it’s no surprise our greatest asset is our people. The impact this creates is an environment that encourages our people to grow and be creative with their talents. This approach builds a culture that is uniquely Olsson. It allows us to grow our people as we grow our business. This, in turn, creates a lasting impact on the world around us. Job Description As a Geotechnical Engineer, you will perform geotechnical engineering design, project proposal and management duties with integrity and a drive to succeed.  You will also process design calculations, develop project scopes and schedules, and travel to job sites for observation. You will be required to ensure high client service by providing constructible designs, meeting deadlines and creating clear communications with our clients. Qualifications You are passionate about: Working collaboratively with others. Having ownership in the work you do. Using your talents to positively affect communities. You bring to the team: Strong communication skills Ability to contribute and work well on a team Bachelor's Degree in civil/geotechnical engineering required Must be a registered professional engineer 6+ years of related Geotechnical analysis and design experience The ability to work in a constant state of alertness and safe manner #LI-DD1 Additional Information Olsson specializes in engineering and design, client advisory services, planning, field services, and environmental. Improving the world has been our mindset from the very beginning, back when Olsson first opened for business in 1956. And it will be our mindset for years to come. As an Olsson employee, you will: Receive a competitive 401(k) match Be empowered to build your career with tailored development paths Have the possibility for flexible work arrangements Engage in work that has a positive impact on communities Participate in a wellness program promoting balanced lifestyles In addition, full-time employees will receive our traditional benefits package (health care, vision, dental, paid time off, etc.) and the opportunity to participate in a bonus system that rewards performance. Olsson is an Equal Opportunity Employer. We encourage qualified minority, female, veteran, and disabled candidates to apply and be considered for open positions. We do not discriminate against any applicant for employment or any employee because of race, color, religion, national origin, sex, sexual orientation, gender identity, gender, disability, age, military status, or other protected status. Olsson understands the importance of privacy and is committed to protecting job applicants’ personal information. Pursuant to the California Consumer Privacy Act, as amended by the California Privacy Rights Act (collectively, the “CCPA”), this notice explains Olsson’s practices regarding the collection, use, and disclosure of personal information for job applicants residing in California. Please read this Notice carefully to understand our privacy practices. For more information about the types of information we collect and how we use it in connection with your general access and use of our website, please review our general California Privacy Notice here .

Location: Remote, Canada; Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As Director of Engineering, Database Excellence, you will lead the Database Excellence stage and report to the Vice President of Data Engineering, in our Engineering department. You will set the direction for the databases that support GitLab.com, self-managed GitLab, and GitLab Dedicated. Your organization owns the reliability, capacity, health, and sustainability of GitLab's application datastores, with PostgreSQL as the primary focus alongside Redis, OpenSearch, ClickHouse, and object storage. You will be accountable for database capacity and availability on GitLab.com. You will guide long-range platform decisions while partnering with Infrastructure, Product, Distribution, Support, Security, and FinOps. The work includes reducing infrastructure costs, improving data growth controls and saturation detection, standardizing database services, rebuilding the self-managed upgrade experience, and contributing to GitLab's company-wide datastore strategy, including Cells partitioning. Learn more in the Database Excellence handbook . What you’ll do Set direction and long-range plans for the database platform in line with company strategy. Own database capacity and availability outcomes for GitLab.com, and communicate database risk to senior leadership. Lead and coach three Engineering Managers and develop Staff-plus engineers into leaders. Drive cross-division technical decisions using data, partnering with Infrastructure, Product, Distribution, Support, Security, and FinOps. Partner with Product to prioritize database platform investments and make infrastructure tradeoffs in the context of product outcomes. Own the hiring plan, organization design, retention, and performance management for teams across regions. Set quarterly objectives and key results and engineering key performance indicators for the stage, and manage the database infrastructure budget with FinOps. Serve as an escalation point for database incidents, standardize effective processes, and keep the handbook current. Take part in on-call or operational support where your team’s ownership model requires it. What you’ll bring Experience leading managers of managers, including coaching newer managers. Experience working hands-on with PostgreSQL at scale, including capacity planning, upgrades, replication, performance, and large production clusters. Experience using data to make strategic technical decisions and connect database platform investments and tradeoffs with product outcomes. Experience supporting software that customers run themselves, or working through the self-managed upgrade challenge. Experience leading backend engineering teams that own production infrastructure, applying site reliability engineering practices including service level objectives, on-call operations, incident management, cost management, and automation. Experience communicating clearly in writing in an asynchronous, handbook-first company. Experience leading globally distributed remote teams. Experience with Redis, ClickHouse, OpenSearch, object storage, Google Cloud Platform, Kubernetes, Terraform, Chef, managed database services, FinOps, AI-assisted engineering workflows, or platform and infrastructure organizations is a plus. About the team Database Excellence includes Database Architecture, Database Automation, and Self Managed Database. Three Engineering Managers lead these teams, alongside an Operations Manager. Our teams work across the United States, Europe, the Middle East, Africa, and India. Database Architecture provides datastore guidance, data growth controls, and the database review process. Database Automation builds PostgreSQL fleet automation, upgrades, provisioning, health monitoring, and an internal managed PostgreSQL offering. Self Managed Database is rebuilding the upgrade and migration experience for self-managed GitLab. Together, we make database services more reliable, sustainable, and easier to operate across GitLab's deployment models. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $230,400 — $345,600 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Colombia, Huila, Colombia; São Paulo, Brazil About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Engineering Manager, (Technical Team Lead) Location: Remote, Brazil, Colombia, Argentina Status: Full-time Role Overview As a Engineering Manager (Technical Team Lead) at Turing, you will be a pivotal part of our engineering leadership for AI initiatives. This is not a pure people management role. You will be expected to be hands on: able to operate on the data, organize the stack, set up infrastructure, and personally deliver a project if no one else is available. You will lead a team through a rapid iteration, startup paced environment with high expectations and short timelines, while also driving the technical vision and execution of complex projects. The ideal candidate is someone who takes ownership beyond what their role technically requires, has a track record of delivering under pressure, and is excited by the intensity of a startup environment rather than looking for a more comfortable, longer timescale management track. Key Responsibilities Hands On Technical Ownership: Operate directly on data, organize the technical stack, and set up infrastructure as needed. Be the technical fallback who can personally deliver a project if no one else is online to do it. Guide the team in architecture, design, and implementation of scalable, maintainable codebases. Build and use AI agents, and review the team's agent based work, since this role requires evaluating agent output directly rather than only reviewing human written code. Prompt Generation and Innovation: Lead innovation efforts in product features and processes. Manage idea and prompt collection and prioritization. Drive experimentation and iteration for optimal outcomes. Work Stream Assignment Across Pods: Allocate projects based on skills and performance. Prioritize tasks to meet business goals. Coordinate with cross functional teams for project alignment. Team Throughput and Task Completion: Monitor progress using metrics like velocity and cycle time. Optimize development processes for efficiency. Ensure tasks meet quality standards before deployment. Spot Reviews on Tasks: Perform quality control through task reviews. Provide actionable feedback to maintain high standards. Identify risks early and adjust processes accordingly. Identifying High and Low Performers: Conduct performance analysis through reviews and metrics. Provide mentorship, feedback, and talent management. Identify high performers for advanced roles and support development for others. People and Project Leadership Under Pressure: Lead teams through urgent, high pressure delivery schedules, not just steady state work. Push delivery timelines in a rapid iteration startup context while maintaining quality. Take direct ownership of outcomes, including being responsive when urgent needs arise. Technical Skills Expert level, hands on experience in Node.js, with React and NestJS specifically, including a deep understanding of their ecosystems, libraries, and best practices. Experience with Infrastructure as Code (IaC) and GCP, including setting up and operating cloud infrastructure directly. Ability to build and use AI agents, with enough fluency to review and evaluate a team's agent built work. Experience leading projects that leverage Python for data analysis, backend development, or AI and ML applications, as well as JavaScript and TypeScript for front end and full stack development. Proficiency in data analysis techniques, including statistical analysis, data visualization, and data manipulation using tools like Pandas, NumPy, and SQL. Strong focus on data quality, including data validation, cleansing, and enrichment. Qualifications Bachelor's or Master's degree in Computer Science, AI, Data Science, Data Engineering, or a related field. Electrical engineering backgrounds are acceptable. AI or data science background is a meaningful plus (helps significantly) but not a dealbreaker if diligence and ownership are evident elsewhere. 5+ years of experience in software development. At least 2 years in a leadership role is the bar for an Engineering Manager managing teams of 3 to 6. For this Senior Engineering Manager (Technical Team Lead) role, candidates should have experience managing meaningfully larger teams than that, not just the 5 to 6 person threshold. Proven track record of leading teams through urgent, complicated technical work, ideally in a startup setting, and delivering on time or ahead of time. Demonstrated ownership that exceeds role expectations: a history of taking on more than the job required, high personal output, and strong initiative. Strong problem solving skills, with a focus on delivering results in a fast paced, dynamic environment. Excellent communication and interpersonal skills, with the ability to collaborate effectively across cross functional teams. What Success Looks Like You are the kind of leader who is excited to do this work. You know it will be intense, that you'll manage a lot of people, process, and technology at once, and you welcome it. You have taken on something genuinely hard, led people through it, and can clearly explain what made it difficult and how you delivered — a truly hard technical milestone, reached in a leadership setting, that you're proud of. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers people to perform at a higher level through a deeper understanding of their bodies and daily lives. As a Senior Manufacturing Engineer, you will be responsible for developing manufacturing solutions that enable new products from early design through production ramp. You will work closely with Product Design, Mechanical, Electrical, Quality, and factory engineering teams to influence product design, define assembly strategies, and develop the fixtures, tooling, equipment, and processes required to manufacture products at scale. You will apply strong mechanical design fundamentals, DFX expertise, and hands-on manufacturing experience to identify manufacturing risks early, translate product requirements into robust manufacturing solutions, and solve complex technical challenges with a high degree of independence. RESPONSIBILITIES: - Partner closely with Product Design and Mechanical Engineering throughout product development to drive DFM, DFA, and DFX, identifying manufacturing risks and influencing product designs for assembly, quality, scalability, automation, and cost. - Develop manufacturing concepts and assembly strategies from early prototypes through production, translating product requirements into practical manufacturing processes, tooling, and equipment. - Design and develop fixtures, tooling, nests, gauges, and manufacturing aids used for assembly, alignment, bonding, welding, inspection, and test. - Own fixture and tooling development from concept through detailed design, tolerance analysis, fabrication, debugging, qualification, and release to production. - Apply GD&T, tolerance analysis, datum strategies, and mechanical design principles to ensure product and fixture designs support repeatable manufacturing and measurement. - Work with product engineering teams to evaluate component interfaces, assembly sequences, joining methods, tolerance stack-ups, and other design characteristics that impact manufacturability. - Develop and qualify manufacturing processes including adhesive bonding, heat staking, mechanical assembly, and other processes used in small consumer electronic devices. - Define critical process parameters, process windows, acceptance criteria, and controls to ensure manufacturing processes consistently meet product requirements. - Evaluate manual and automated assembly approaches and develop fixture and equipment concepts that enable appropriate levels of automation. - Use DOEs, process characterization, capability analysis, and failure analysis to validate manufacturing designs and optimize process performance. - Troubleshoot complex product, fixture, and process interactions during development builds and production ramp, driving issues to root cause and implementing robust engineering solutions. - Collaborate directly with contract manufacturers, equipment vendors, and tooling suppliers to develop, review, debug, and qualify manufacturing solutions. - Provide hands-on technical support during prototype builds, engineering validation, process qualification, and production ramp. - Travel domestically and internationally up to 25% to support development builds, tooling and equipment qualification, and production ramp. QUALIFICATIONS: - Bachelor's degree in Mechanical Engineering, Manufacturing Engineering, or a related technical field. - 5+ years of manufacturing engineering experience supporting product development and moderate- to high-volume manufacturing. - Ability to operate independently in fast-paced product development and manufacturing environments with limited technical oversight. - Strong experience applying DFM, DFA, and DFX principles during product development, with demonstrated ability to influence product designs for manufacturing. - Strong proficiency in SolidWorks, including detailed mechanical design, assemblies, drawings, and fixture design best practices. - Strong knowledge of GD&T, datum structures, tolerance analysis, tolerance stack-ups, and dimensional inspection methods. - Experience developing and qualifying manufacturing processes for small electromechanical or battery-powered electronic products. - Experience with manufacturing technologies including adhesives, heat staking, injection-molded plastics, and automated and manual assembly. - Strong understanding of injection-molded plastic part design principles, tooling considerations, defect modes, and their interaction with downstream assembly processes. - Experience designing and executing DOEs, process characterization studies, capability analyses, and structured failure investigations. - Strong hands-on mechanical/electrical and analytical problem-solving skills with the ability to independently develop solutions to complex product and manufacturing challenges. - Experience working directly with contract manufacturers, equipment vendors, tooling suppliers, and cross-functional product development teams. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.   Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.   WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.   The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.   At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.   The U.S. base salary range for this full-time position is $150,000 - $195,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.   In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.   These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet. Key responsibilities Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale Own end-to-end performance of our largest RL runs, from research code down to the hardware Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause Minimum qualifications Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack Preferred qualifications Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise Experience developing RL algorithms for language models Experience with scaling laws or other quantitative models of training efficiency Experience designing or modifying transformer architectures beyond standard configurations Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices Experience with C++ or Rust Representative projects Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Chicago 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. About the Role We're looking for an AI Inference Platform Engineer to build, operate , and optimize the systems that serve large language , vision, multimodal, and embedding models across DRW. This role provides DRW's firmwide interface to modern AI models, from early evaluation through reliable production use . You'll work across inference runtimes, distributed systems, and production platform engineering, with deep GPU literacy.  You'll own the serving platform end-to-end: onboarding newly released models, measuring quality and performance equivalence across serving configurations, scheduling workloads across tenants, and continuously improving latency, throughput, utilization, reliability, and cost across the inference fleet. What You'll Do Optimize LLM inference performance across modern NVIDIA GPU architectures and inference runtimes. Build end-to-end performance profiling and observability to identify bottlenecks from individual GPU kernels through multi-node inference systems. Design and optimize KV cache and distributed inference architectures, including caching, routing, memory tiering, and prefill/decode strategies. Own day-0 model onboarding, determining the appropriate runtime, precision, sharding, memory, batching, cache policy, and serving configuration for new models. Maintain validated performance profiles for important model and hardware combinations, including performance and quality regression testing. Measure and monitor quality equivalence across serving configurations, including KV cache quantization, speculative decoding acceptance thresholds, precision choices, and model routing, so in-house serving can be trusted to match reference-model quality on production workloads. Manage the production serving lifecycle of models, including versioning, compatibility, staging, canarying, promotion, rollback, and retirement. Partner with SRE and platform teams to automate model deployment, distribution, production readiness, observability, and reliable operation across environments. Optimize model placement, scaling, and resource allocation across the inference fleet to improve utilization and cost efficiency while meeting performance and reliability requirements. Design and operate multi-tenant scheduling and isolation across shared GPU capacity, balancing latency SLOs, throughput, and priority across concurrent workloads. What We're Looking For The Tech Hands-on experience serving LLMs on NVIDIA GPUs, with familiarity across current and emerging architectures (Hopper, Blackwell, and successors), HBM, Tensor Cores, NVLink / NVSwitch , and the compute and memory bottlenecks that shape serving decisions . Deep expertise in at least one modern inference runtime such as TensorRT-LLM, vLLM, or SGLang. Practical knowledge of inference optimization techniques including continuous batching, scheduling, chunked prefill, speculative decoding, quantization, CUDA Graphs, and paged attention. Understanding of KV cache architecture, including prefix caching, block management, sizing, eviction, quantization, cache-aware routing, and multi-tier caching. Experience measuring model quality equivalence across serving configurations, including evaluation harnesses, task-specific benchmarks, and regression detection for quantization, KV cache, and speculative decoding changes. Experience designing and tuning distributed inference systems, including tensor parallelism, multi-node deployments, and disaggregated prefill and decode. Experience with multi-tenant GPU scheduling, workload isolation, and QoS across concurrent inference workloads. Proficiency with GPU performance and observability tooling such as Nsight, DCGM, OpenTelemetry, Prometheus, and Grafana. Strong Linux and systems performance fundamentals, with the ability to diagnose bottlenecks across hardware, drivers, runtimes, networking, and application layers. Production experience with model serving infrastructure, including CI/CD, automated testing, observability, and production readiness. The Intangibles You take a measurement-driven approach to performance optimization. You take ownership of performance problems across hardware, runtime, model, and infrastructure boundaries. You can move quickly and reprioritize as trading needs change, while maintaining a high bar for production systems. You understand the importance of reliability, predictability, and performance when AI systems are integrated into trading workflows and decision-making processes. You can evaluate unfamiliar models, runtimes, and hardware quickly and make sound engineering decisions with limited prior guidance. You communicate clearly and can explain complex performance tradeoffs across engineering teams. The annual base salary range for this position is $200,000 to $250,000 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-VD1]