Senior Staff Machine Learning Engineer

Who you are

• 9+ years of experience in ML engineering, applied ML, or a related field, with substantial production impact
• Senior Staff-level technical leadership and a record of influencing direction across teams
• Deep modeling expertise and strong fundamentals in data, objectives, architectures, optimization, evaluation, and experimentation
• Concrete examples of using agentic AI or AI coding tools to produce better ML outcomes—not just faster code generation
• Ability to design guardrails and evaluations that make autonomous or semi-autonomous workflows trustworthy
• Strong coding ability in Python and fluency with modern ML frameworks and data tooling
• Clear, concise communication and a collaborative, low-ego approach to technical leadership

What the job involves

• We are seeking a Senior Staff Machine Learning Engineer to define and accelerate how we leverage agentic AI to transform machine learning models and modeling workflows
• This high-impact role focuses directly on modeling leverage—enhancing model quality and velocity—rather than building general infrastructure
• As a senior staff engineer, you will operate as a technical leader across the entire modeling loop, utilizing modern AI-assisted development systems (such as Claude, Cursor, and comparable tools) to make model development faster, more rigorous, and significantly more effective across the organization
• Why Join Us?
• This role offers a domain-flexible platform to redefine how ML engineering is done
• If you are passionate about combining deep ML fundamentals with the vanguard of agentic AI to multiply team impact, we’d love to talk to you
• What you’ll do:
• Develop advanced ML models and the agentic workflows that help teams design, train, evaluate, and improve them
• Use tools such as Claude, Cursor, and comparable AI-assisted development systems to investigate model behavior, generate and test hypotheses, automate repetitive analysis, and improve code and experiments
• Build reliable loops for data and label analysis, feature and embedding development, architecture comparison, hyperparameter optimization, error analysis, and experiment readouts
• Design evaluation harnesses that measure both model outcomes and agent/workflow quality, including correctness, reproducibility, regressions, cost, and latency
• Partner with product, data science, ML infrastructure, and engineering teams to connect modeling improvements to user and business outcomes
• Set technical direction across teams, mentor engineers, and raise the bar for modeling rigor and AI-assisted engineering

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits