Founding Machine Learning Engineer (San Francisco)

Our Client is building a next-generation voice-AI platform that powers curated introductions, agentic scheduling, and real-time feedback.

Were seeking a founding-calibre ML Engineer to own our machine learning systems end-to-end from data pipelines and model training to evaluation and low-latency inference.

Youll design, build, and ship ranking and recommendation systems that make every match feel more personal and improve week after week.

What Youll Do

• Design and deploy multi-stage retrieval and re-ranking systems for compatibility, search, and personalisation.
• Build and maintain data pipelines for training, evaluation, and reporting ensuring reproducibility and quality.
• Train and fine-tune LLMs / encoders , manage model versioning, rollout, and rollback.
• Run offline metrics (AUC, NDCG, MAP) and online A / B tests to measure real-world impact.
• Build inference services that meet tight latency and cost targets, with caching and fallback strategies.
• Implement guardrails and monitoring for drift, bias, and reliability; define model SLOs and alerts.
• Collaborate across ML, platform, and product teams to turn voice and text signals into better matches .

Tech Stack

Python PyTorch Hugging Face OpenAI / Anthropic APIs pgvector / FAISS / Pinecone Postgres

Airflow / Prefect / dbt AWS (S3, ECS / Kubernetes, Lambda) CI / CD (GitHub Actions)

What Were Looking For

Bonus Points