Senior Machine Learning Engineer

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.

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, and 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. We're looking for an exceptional Senior Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences.

You’ll take on a highly influential role shaping vision and execution across key strategic initiatives. You’ll partner with cross‑functional leaders, contribute to high‑impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting‑edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them.

This role is based in our Menlo Park, CA, with in‑person attendance expected at least three days per week.

• Fine tune, product use cases, develop AI inference services for product use cases.
• Optimize agentic workflows through fine tuning and reinforced learning.
• Improve evaluation and training or finetune models for product use cases.
• Work with platform tooling team to product ionize fine‑tuned models for customer use cases.

• Bachelor's degree or foreign equivalent in Computer Science or related field and three (3) years of experience in AI, Machine learning, or a related occupation.
• Designing and deploying machine learning systems using production‑grade frameworks for training, optimization, and service integration.
• Implementing scalable data pipelines using distributed compute frameworks to process, validate, and version large datasets.
• Building and fine‑tuning large language models using techniques such as LoRA, RLHF, retrieval augmentation, and prompt optimization.
• Developing high‑performance inference systems by applying quantization, pruning, distillation, and hardware‑aware optimization.
• Engineering retrieval‑augmented generation pipelines with vector indexes, embedding workflows, and document‑chunking strategies.
• Authored production‑ready microservices that expose model inference endpoints with robust API design, containerization, and orchestration.
• Constructing automated AI evaluation frameworks including metric suites, safety checks, hallucination detection, and regression testing.
• Background checks required.

• 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, including 100% paid health insurance for employees and 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 work spaces.

$133,952 - $245,000 per year

At Robinhood, we care deeply about diverse representation in our workforce as it supports our mission to democratize finance for all. In support of this goal, we encourage applicants to voluntarily identify demographic information. This information helps us to continue building a more inclusive workplace and to ensure effective recruiting programs that are inclusive of individuals across all backgrounds.

Self‑identifying in this section is completely voluntary…