- Страна
- США
- Зарплата
- 209 000 $ – 245 000 $
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Senior Machine Learning Engineer, Agentic
Превосходная вакансия в топовой финтех-компании с конкурентной зарплатой, отличным пакетом льгот и возможностью работать над передовыми технологиями ИИ.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний в области LLM, методов оптимизации (DPO, PPO) и архитектуры автономных агентов, а также высокими стандартами производительности в Robinhood.
Анализ зарплаты
Предлагаемая зарплата ($209k - $245k) находится на верхнем уровне рыночного диапазона для Senior ML ролей в таких технологических хабах, как Менло-Парк и Белвью. С учетом бонусов и акций совокупный доход значительно превышает средние показатели по рынку.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Machine Learning Engineer position within the Agentic team at Robinhood. With extensive experience in developing and deploying Large Language Models and a deep understanding of agentic workflows—including reasoning loops and tool invocation—I am eager to contribute to your mission of democratizing finance. My background in implementing optimization techniques like DPO and PPO, combined with a focus on building rigorous evaluation harnesses, aligns perfectly with your team's goals.
In my previous roles, I have successfully led complex technical projects, translating product objectives into high-performance production systems. I am particularly drawn to Robinhood's commitment to innovation and the challenge of applying frontier AI technologies to the world’s most significant financial problems. I am confident that my technical expertise in ML orchestration and my bias toward action will allow me to make an immediate impact on the Agentic team and help drive the next generation of AI-powered financial products.
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Описание вакансии
Join us in building the future of finance.
Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.
About the team + role
We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.
The Agentic team at Robinhood builds and ships production AI agents that power the next generation of AI financial products. Our mission is to rapidly build, evaluate, and deploy high-performance AI agents on production-grade infrastructure, strong evaluation and observability baked in, and continuous optimization support.
This role is based in our Menlo Park, CA and Bellevue, WA offices, with in-person attendance expected at least 3 days per week.
At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.
What you’ll do
- Translate product goals into measurable metrics and SLOs, and build a rigorous evaluation harness to continuously score agents performance
- Develop feedback and optimization pipelines that uses both automated metrics and human-in-the-loop evaluation signals to improve agent behavior over time
- Implement and scale optimization techniques such as Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and reward modeling to improve agent performance.
- Launch and support fine-tuned models in production environments with robust evaluation, rollback strategies, and performance monitoring.
- Collaborate closely with applied AI/ML teams to translate state-of-the-art research in agentic reasoning, planning, and tool use into reliable, production-ready systems
What you bring
- Strong technical expertise in software development, with understanding of agentic workflows—including reasoning loops, tool invocation, memory, and orchestration of autonomous AI agents.
- Hands-on experience using Large Language Models, including prompt engineering, fine-tuning, model distillation, and deploying optimized models (e.g. via DPO, PPO) into production environments.
- Leadership and mentorship capabilities, with a track record of guiding complex technical projects and supporting the growth of teammates through code/design reviews and technical direction.
- Excellent communication and collaboration skills, with the ability to translate technical ideas into actionable plans and work effectively with cross-functional partners, including product and infrastructure teams.
- Innovation mindset and commitment to continuous learning and a bias toward action, staying at the forefront of ML/AI trends, agentic systems research, and best practices in tooling, safety, and evaluation.
What we offer
- Challenging, high-impact work to grow your career
- Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
- Best-in-class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
- Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
- Employer-paid life & disability insurance, fertility benefits, and mental health benefits
- Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
- Exceptional office experience with catered meals, events, and comfortable workspaces
In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. For other locations not listed, compensation can be discussed with your recruiter during the interview process.
Base Pay Range:
Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)
$209,000—$245,000 USD
Zone 2 (Denver, CO; Westlake, TX; Chicago, IL)
$184,000—$216,000 USD
Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)
$163,000—$191,000 USD
Click here to learn more about our Total Rewards, which vary by region and entity.
If our mission energizes you and you’re ready to build the future of finance, we look forward to seeing your application.
Robinhood provides equal opportunity for all applicants, offers reasonable accommodations upon request, and complies with applicable equal employment and privacy laws. Inclusion is built into how we hire and work—welcoming different backgrounds, perspectives, and experiences so everyone can do their best. Please review the Privacy Policy for your country of application.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- Machine Learning
- Large Language Models
- MLOps
- Software Development
- Prompt Engineering
- Model Distillation
- Direct Preference Optimization
- Proximal Policy Optimization
Возможные вопросы на собеседовании
Проверка практического опыта работы с современными методами дообучения моделей.
Можете ли вы описать ваш опыт внедрения Direct Preference Optimization (DPO) или PPO для улучшения поведения ИИ-агентов?
Оценка понимания архитектуры агентных систем.
Как бы вы спроектировали надежную систему памяти и планирования для финансового ИИ-агента, работающего в реальном времени?
Важно для обеспечения качества и надежности в финтехе.
Каков ваш подход к созданию системы оценки (evaluation harness) для измерения галлюцинаций и точности инструментов в агентных рабочих процессах?
Проверка навыков масштабирования и вывода моделей в продакшн.
С какими основными трудностями вы сталкивались при развертывании LLM в высоконагруженных средах и как вы их решали?
Оценка лидерских качеств и умения работать в команде.
Расскажите о случае, когда вам приходилось переводить сложные исследовательские концепции в понятный план действий для кросс-функциональной команды.
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- Страна
- США
- Зарплата
- 209 000 $ – 245 000 $