Machine Learning Tech Lead - Real-time AI

Key Responsibilities

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.

Who are you?

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.