Tech Lead, Machine Learning (Real-time AI)

Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency.

Powered by LOIS, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest-growing technology companies in the country.

The Depo Intelligence team powers the AI behind Filevine's deposition product line. We deliver automated post-deposition transcript analysis, timelines, summaries, and witness impressions. Our latest product, Depo Copilot, assists attorneys during deposition preparation and delivers real-time insights during live proceedings. Additionally, our intelligent insights extend support to the Filevine Meetings and Notetaking apps.

As the Tech Lead for Depo Intelligence, you will drive the technical direction of our AI systems. This is a hands-on machine learning role requiring strong software engineering skills. You will spend your time designing and building LLM pipelines and evaluations, as well as leading the team's architectural and design choices. You will collaborate closely with the team's Engineering Manager, who handles people management and delivery, allowing you to focus squarely on technical leadership.

Own the technical vision and system architecture for machine learning workflows driving Depo Copilot, Depo Summaries, and the Meetings and Notetaking app.

Architect and build LLM pipelines capable of reasoning over extensive case files and real-time transcripts, ensuring all outputs are fully grounded in source material.

Determine optimal model strategies - evaluating when custom fine-tuning or trained models are required over prompt engineering - and lead implementation.

Establish evaluation frameworks alongside our legal experts and annotators, creating custom datasets, rubrics, offline metrics, and release checks to proactively prevent regressions.

Optimize pipeline efficiency, latency, and operational costs for live streaming analysis using strategic model selection, fallbacks, and precomputation.

Transition prototypes into production-grade systems with typed contracts, test coverage, tracing, and observability, serving as technical lead for incident response during live proceedings.

Author design documents, guide architectural decisions, and partner with Product, product engineering, speech recognition, and document retrieval teams to translate roadmap goals into actionable technical plans.

Mentor machine learning engineers through detailed code reviews, pair programming, and design feedback while clearly communicating technical outcomes and tradeoffs to business stakeholders.

Minimum of 5 years of experience building production ML or software systems, including at least 3 years in machine learning roles with end-to-end project ownership.

Proven track record of technical leadership, including conducting design reviews, setting architectural direction, and mentoring engineers.

Strong background in NLP and production LLM systems, encompassing retrieval and grounding, long-context reasoning, structured outputs, evaluation, and mitigation of common failure modes.

Experience designing evaluation frameworks for generative AI systems, including developing datasets and rubrics in collaboration with domain experts and annotators.

Solid software engineering foundations, including API design, data modeling, distributed and asynchronous workflows, testing, and fault tolerance.

Advanced proficiency in Python, alongside hands-on experience with PyTorch or other deep learning frameworks.

Experience with real-time/streaming systems or legal domain NLP is a plus.

Strong written and verbal communication skills in English, a collaborative team spirit, and an autonomous problem-solving mindset.

Ability to meet periodically at our Prague or Bratislava office to support in-person collaboration.

Our current stack primarily utilizes Python (DSPy, Pydantic, Temporal), PostgreSQL, Kubernetes, AWS, Google Cloud, and multi-provider LLMs; strong core engineering skills are prioritized over familiarity with specific tools or frameworks.

Compensation starts at 190,000 CZK per month, with the final offer determined by the candidate’s seniority and experience.