Senior Machine Learning Engineer

EvenUp

Senior Machine Learning Engineer building and deploying ML models for EvenUp's claims-intelligence platform. Collaborating with engineers and data scientists to improve legal outcomes for personal-injury clients.

Posted 7/8/2026full-timeSan Francisco • California • 🇺🇸 United StatesSenior💰 $196,000 - $265,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying machine learning systems across the full lifecycle, with strong capabilities in Python, distributed systems, and API design. Proven ability to mentor engineers and influence technical direction while driving scalability and efficiency in ML workflows.

Highest-signal resume keywords

Machine Learning Lifecycle ManagementPython ProgrammingData Pipeline ArchitectureMentorship and Technical LeadershipNLP and Generative AI Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning SystemsData StrategyModel EvaluationScalable Data PipelinesAPI DesignDistributed SystemsModel DeploymentBenchmarking FrameworksQuality MetricsReal-Time Inference

Soft Skills

Problem FramingCollaborationInfluencing Technical DirectionMentoring

Industry Keywords

NLPInformation RetrievalGenerative AIHuman-in-the-Loop ReviewAutomated Benchmarks

Tech Stack

Tools & technologies

Distributed SystemsPython

About the role

Key responsibilities & impact

Design, build, and own production ML systems across the full lifecycle - problem framing, data strategy, training, evaluation, deployment, and monitoring.
Architect scalable data pipelines that handle structured, unstructured, and embeddings-based data for training and inference.
Build reusable frameworks and infrastructure for model development, evaluation, and benchmarking.
Partner with data scientists and product managers to translate ambiguous business problems into concrete ML system designs.
Apply and productionize state-of-the-art techniques across NLP, information retrieval, and generative AI where the problem calls for it.
Define and implement evaluation strategies - quality metrics, human-in-the-loop review, automated benchmarks - to ensure model reliability.
Drive scalability and efficiency across ML workflows, from large-scale data processing to real-time inference.
Work with ML platform engineers to integrate models and frameworks into production environments.
Document system architectures and establish best practices that other engineers build on.
Mentor other engineers and contribute technical judgment to hiring and calibration as the team grows.

Requirements

What you’ll need

5+ years building and deploying machine learning systems in production.
Strong software engineering fundamentals - Python, distributed systems, API design.
Experience owning the full ML lifecycle, not just model training in isolation.
A track record of turning ambiguous problems into scoped, shippable solutions.
Experience mentoring other engineers and influencing technical direction beyond your own code.
Ability to work hybrid (3 days your choice) in our San Francisco or Toronto Canada office.

Benefits

Comp & perks

Choice of medical, dental, and vision insurance plans for you and your family.
Additional insurance coverage options for life, accident, or critical illness.
Flexible paid time off, sick leave, short-term and long-term disability.
10 US observed holidays, and Canadian statutory holidays by province.
A home office stipend.
401(k) for US-based employees and RRSP for Canada-based employees.
Paid parental leave.
A local in-person meet-up program.
Hubs in San Francisco and Toronto.