Senior Machine Learning Engineer — San Francisco, CA — LinkedIn — 4490239227

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EvenUp

EvenUp

Senior Machine Learning Engineer

San Francisco

Full-time

At a glance

$170k–$250k / yr

Location

San Francisco

Job type

Full-time

Experience level

Mid-Senior ICs

Posted

Today

Apply before

December 30, 2026

• 45 days left

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Company

EvenUp is the leading proactive AI platform for personal injury law, the first of its kind, built on the largest personal injury dataset. Trusted by 2,000+ firms, EvenUp helps legal teams run casework like clockwork—drafting documents, automating workflows, and maximizing the outcome of every case from intake to resolution.

Industry

Technology

Company size

251-1000

Headquarters

San Francisco, California

Funding

Private

WebsiteLinkedIn

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Job Description

EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.

We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at ••••••••

Senior Machine Learning Engineer

At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise in data quality, robust model development, and ongoing innovation.

We're looking for a Senior Machine Learning Engineer to help build the models and systems powering Piai™, our proprietary claims-intelligence platform. You'll work across the ML stack - from data pipelines to production model deployment - alongside ML engineers, data scientists, and legal subject-matter experts to turn raw legal and medical data into systems that directly improve outcomes for personal-injury clients.

What You'll Do

• 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.

What You Bring

• 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.

Nice to Have

• Experience with NLP, LLMs, or generative AI - embeddings, fine-tuning (LoRA or other PEFT), or prompt engineering.

• Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch) or orchestration frameworks (LangChain, LlamaIndex).

• Experience with evaluation methodologies for generative AI - RAG benchmarks, hallucination reduction, factual grounding.

• Experience in a high-growth startup environment.

• Background in legal tech, healthcare, or other high-stakes, regulated domains.

Benefits & Perks

As part of our total rewards package, we offer attractive benefits and perks to our employees, including:

• 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.

(Please note the above benefits & perks are for full-time employees)

Notice To Candidates

To ensure fairness and proper consideration, we do not accept resumes or expressions of interest via email or social media messages. If you’re interested in a role, please submit your application directly through our careers page.

Please note that EvenUp may use AI notetakers and other recording devices in the recruiting process. If you interview with us, with your consent, we may record your conversations and summarize them into notes for internal use. Recording is optional, and declining will not affect your candidacy.

EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Compensation Range: $170K - $250K

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Our Partners give vQJC 67XyYe' applications priority review as they source candidates.

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