Founding Machine Learning EngineerKey Technology • Fremont, California, United States
Founding Machine Learning Engineer
Key Technology • Fremont, California, United States
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.full_time]
[job_card.job_description]PyTorch Hugging Face OpenAI / Anthropic APIs pgvector / FAISS / Pinecone Postgres AWS (S3, ECS / Kubernetes, Lambda) CI / CD (GitHub Actions) 3+ years of experience in applied ML (ranking, search, or recommendations). Deep Python skills and experience with PyTorch or TensorFlow (Hugging Face a plus). Hands-on with embeddings, LLMs, and vector search (pgvector, FAISS, Pinecone, Weaviate). Proven ability to take models from notebook to production — APIs, CI / CD, monitoring, rollback. Experience building data pipelines with Airflow, Prefect, Dagster, or dbt. Comfortable owning latency, cost, and reliability for inference services. Must be authorised to work in the U.S. and onsite 5 days / week in San Francisco. Experience with real-time or voice systems , retrieval / re-ranking stacks, or feature stores. Prior work at consumer AI, dating, or recommendation startups where you shipped ranking systems.
Our Client is building a next-generation voice-AI platform that powers curated introductions, agentic scheduling, and real-time feedback.
We’re 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.
You’ll design, build, and ship ranking and recommendation systems that make every match feel more personal and improve week after week.
What You’ll 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
Airflow / Prefect / dbt
What We’re Looking For
Bonus Points
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