Senior Machine Learning Engineer

Robinhood

Hybrid

Quick summary

Work type: Hybrid
Location: Menlo Park, CA
Salary: $133,952–$245,000 / yr
Posted: 1 day ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $212k

This role $189k

$120k most similar roles pay here $260k

This role pays less than 64% of similar roles. Most pay $178,012–$246,150 — the shaded band above. At the midpoint, this role pays about $189k versus about $212k for comparable roles.

Based on 240 similar postings.

Employer

About Robinhood

Robinhood is a financial technology company offering commission-free stock, ETF, options, and cryptocurrency trading through its mobile app, aimed at democratizing access to financial markets. Industry: Financial Technology & Investment App

Robinhood currently has 78 open roles on FindRole.

Listed pay typically runs $187,000–$220,000 across 77 roles with salary data.

Most-posted roles

View all roles at Robinhood

At a glance

TL;DR · Senior Machine Learning Engineer

Apply Now Log in to save

Join our team as a Senior Machine Learning Engineer in Menlo Park, where you will work closely with the platform tooling team to fine-tune and optimize large language models for product use cases. Your day-to-day responsibilities include developing AI inference services, implementing scalable data pipelines, and engineering retrieval-augmented generation pipelines using vector indexes and embedding workflows. You will also build automated evaluation frameworks and production-ready microservices that expose model inferences with robust API design. Required skills encompass designing machine learning systems with production-grade frameworks, applying techniques like LoRA, RLHF, and prompt optimization, and deploying high-performance inference systems through quantization and hardware-aware optimization. This role involves working on large-scale AI solutions to enhance customer experiences across various products.

Skills

Python TensorFlow PyTorch Kubernetes Docker CI/CD Prometheus Grafana PostgreSQL AWS Azure Google Cloud Platform LoRA RLHF Vector indexes Embedding workflows Document-chunking strategies

What you'll do

Fine-tune and optimize machine learning models for product use cases.
Develop high-performance inference systems using quantization, pruning, and distillation techniques.
Build scalable data pipelines with distributed compute frameworks to process large datasets.
Engineer retrieval-augmented generation pipelines including vector indexes and embedding workflows.
Collaborate on productionizing fine-tuned models for customer-facing applications.

What we're looking for

Bachelor's degree in Computer Science or related field and 3 years of AI/Machine Learning experience.
Experience designing and deploying machine learning systems using production-grade frameworks.
Expertise in implementing scalable data pipelines with distributed compute frameworks.
Proficiency in building and fine-tuning large language models using advanced techniques.
Skill in developing high-performance inference systems through various optimization methods.
Authored production-ready microservices exposing model inference endpoints with robust API design.