Staff Machine Learning Engineer
New
E
EvenUpLegal Tech
Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs.Full-TimeStaff
Salary$212K - $301K; San Francisco: Base Salary $223K – $301K; Toronto: Base Salary CA$182K – CA$246K; Remote US: Base Salary $212K – $287K; Remote Canada: Base Salary CA$178K – CA$241K
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonData AnalysisMachine LearningNLP
Requirements
- 7+ years of hands-on ML engineering experience with multiple models shipped to production.
- Deep expertise in Machine Learning, NLP, and LLMs.
- Demonstrated ability to set technical strategy and drive execution in ambiguous, fast-moving environments.
- Track record of mentoring senior engineers and raising technical standards.
- Experience partnering directly with Product and Engineering leadership.
- High proficiency in Python.
- Strong command of modern ML and NLP frameworks.
- PhD in Machine Learning, Computer Science, or a related quantitative field (nice to have).
- Experience with document understanding, entity/relationship extraction, or structured extraction (nice to have).
- Experience with LLM fine-tuning techniques like LoRA, QLoRA, or RLHF/RLVR (nice to have).
- Experience in a high-growth startup environment (nice to have).
Responsibilities
- Set technical strategy for a broad area of the ML roadmap, translating ambiguous business and research goals into scoped, production-ready systems.
- Tackle complex modeling problems such as long-context understanding, multi-document analysis, and complex reasoning.
- Apply advanced ML techniques like fine-tuning, reinforcement learning, and retrieval while balancing innovation with practical system constraints.
- Establish rigorous evaluation standards to reduce hallucinations, improve factual consistency, and define quality benchmarks.
- Drive data excellence by performing hands-on analysis of training and evaluation data to manage noise and drift at scale.
- Provide technical leadership and mentorship to the ML team, raising the bar for experimentation and engineering rigor.
- Act as a bridge between research and production, ensuring new techniques are successfully integrated into shippable products.
- Partner cross-functionally with product, engineering, and legal subject-matter experts to define technical direction.
- Scale machine learning systems cost-effectively while ensuring they remain grounded in business and customer needs.
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