Senior Machine Learning Engineer

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. We're looking for an exceptional Senior Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents.

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.

What you'll do

• Fine tuning, product use cases, developing AI inference services for product use cases.
• Optimization of agentic workflows through finetuning and reinforced learning.
• Improving evaluation and training or finetune models for product use cases.
• Working with platform tooling team to productionize fine-tuned models for customer use cases. Telecommuting Permitted.

What you bring

• Bachelor's degree or foreign equivalent in Computer Science or related field and three years (3) of experience in AI, Machine learning, job offered or related occupation.
• Education and/or experience must include 3 years of experience with each of the following skills is required:
  • 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