Lead the design and architecture of large-scale ML systems for retrieval-augmented generation (RAG), vector search, and fine-tuning frameworks across multiple product lines.
Define and drive technical strategy and best practices for ML system design, including embedding pipelines, evaluation frameworks, and integration with vector databases.
Mentor and guide other engineers by reviewing designs, code, and system proposals to elevate the technical bar across the ML engineering org.
Partner with product, research, and infra teams to translate ambiguous business and research goals into robust ML system architectures.
Drive innovation and prototyping in areas such as semantic search, generative AI evaluation, and fine-tuning techniques, with a focus on production-readiness.
Own the frameworks and abstractions that make ML workflows reproducible, scalable, and reusable across the company.
Establish standards for system evaluation, including relevance, latency, cost efficiency, and reliability metrics, and ensure they are consistently applied.
Act as a bridge between applied ML research and engineering, ensuring that new techniques (LoRA, retrieval optimizations, etc.) are integrated into production frameworks effectively.
Influence long-term roadmap and platform direction by identifying gaps in ML tooling, infrastructure, and developer experience.
Represent the ML engineering team in cross-org architectural reviews, ensuring alignment with platform, data, and infra strategies.

Requirements

5+ years of experience in machine learning with multiple models deployed in operational settings
Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch)
Strong software engineering skills (Python, distributed computing, APIs)
Strong knowledge of transformer models (LLMs, embeddings, fine-tuning methods like LoRA, PEFT)
Understanding of evaluation methodologies for generative AI (RAG benchmarks, hallucination reduction, factual grounding)
Strong proficiency with the latest Large Language Model (LLM) technologies
Expertise in one or more areas of machine learning, such as deep learning, reinforcement learning, probabilistic modeling, or optimization
Strong communication, collaboration, and coaching skills
High proficiency in a procedural programming language (e.g. Python)
Ability to translate and apply cutting edge research into practical solutions
Strong leadership and mentorship abilities, with a passion for guiding and developing other team members
Experience working in a high-growth startup environment.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningvector databasesPythondistributed computingAPIstransformer modelsfine-tuning methodsevaluation methodologiesdeep learningreinforcement learning

Soft Skills

communicationcollaborationcoachingleadershipmentorship