- San Francisco, California, United States
Location: San Francisco, CA or New York, NY
Work Model: Hybrid
Employment Type: Full-Time
Compensation: $250,000 to $300,000 base + equity
Overview
An early-stage technology company is building machine learning systems for complex, high-consequence government applications. The problems are the kind where clean benchmarks do not exist: ground truth is often limited, requirements shift as the mission evolves, and the people using the output need to understand why the model said what it said.
The role owns ML systems end to end, from working with end users to define the actual problem, through experimentation and approach selection, to production deployment and ongoing monitoring. It sits at the intersection of research depth and engineering rigor, and a central part of the job is deciding when machine learning is the right tool and when a simpler statistical or software approach will serve users better.
The position suits someone who wants full ownership of outcomes rather than a slice of a pipeline, and who values models that are trusted and acted upon over models that are merely sophisticated.
What You’ll Do
- Partner with end users and engineers to understand the underlying problem before committing to a technical approach
- Identify, evaluate, and work with complex public and private datasets, including incomplete or sparsely labeled data
- Design, test, and compare modeling approaches spanning classical ML, deep learning, probabilistic methods, causal techniques, and generative AI
- Architect and deploy end-to-end production ML systems, then integrate them into broader software products and workflows
- Build and maintain MLOps pipelines and monitor deployed models for drift, degradation, and other production issues
- Develop models whose outputs are interpretable enough to be understood, trusted, and acted upon
- Communicate technical approaches, limitations, and results clearly to technical and nontechnical stakeholders
What We’re Looking For
- Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Physics, or a related technical field, or equivalent experience building production ML systems
- Experience architecting end-to-end machine learning systems deployed to real users
- Strong ML fundamentals and a track record of evaluating competing modeling approaches
- Hands-on experience across multiple ML paradigms, such as classical ML, deep learning, probabilistic modeling, and generative AI
- Experience with imperfect, incomplete, or sparsely labeled real-world datasets
- Demonstrated ability to take models from experimentation through production deployment and monitoring
- Strong technical judgment and comfort making decisions under ambiguity
- Ability to balance model performance against interpretability and practical usefulness
- Strong written and verbal communication skills
Preferred
- Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical discipline
- Prior ML work in a startup or similarly high-ownership environment
- Experience explaining sophisticated technical work to nontechnical audiences
- Prior exposure to government or defense applications
Compensation
$250,000 to $300,000 base salary plus equity, depending on experience and qualifications. Competitive benefits are provided.