AI ML Engineer

What we're looking for

Known is building a voice-AI product that powers curated introductions, agentic scheduling, and post-date feedback. You will own the ML systems that make matches feel accurate and improve every week—from data and features to training, evaluation, and low-latency inference—working closely with platform and product.

Our stack: Python, PyTorch, Hugging Face, OpenAI/Anthropic APIs, embeddings and vector search (pgvector/Pinecone/FAISS), Postgres + warehouse for analytics, Airflow/Prefect/dbt for pipelines, online experimentation/A/B testing, observability for models and services on AWS (S3, ECS/Kubernetes, Lambda). CI/CD with GitHub Actions.

• Design and ship multi-stage retrieval + re-ranking for compatibility scoring, search, and personalization.

• Build and maintain data/feature pipelines for training, evaluation, and reporting; ensure reproducibility and data quality.

• Train, fine-tune, or prompt LLM/encoder models; manage model versioning, rollout, and rollback.

• Run offline evaluation (e.g., AUC, NDCG, MAP) and online experiments to measure real user impact.

• Stand up inference services with tight p95 latency and cost targets; add caching, batching, and fallback strategies.

• Implement safety/guardrails and monitoring for drift, bias, and failure modes; define model SLOs and alerts.

• Collaborate with infra/platform to productionize models and with product/design to turn signals from voice/text into better matches.

• Document decisions, write lightweight runbooks, and share dashboards that track match quality and model health.

We are hiring a founding-caliber Infrastructure / Platform Engineer who has owned production cloud environments and data platforms in high-growth settings. You will set the golden paths for services, data, and model delivery, and you are comfortable working on-site in San Francisco five days a week.

• 4 to 10+ years in infrastructure, platform, or data engineering with real ownership of uptime, performance, and security.

• Expert with AWS and Infrastructure-as-Code (Terraform, Pulumi, or CloudFormation).

• Strong proficiency in Python or TypeScript, plus tooling/scripting (Bash/YAML).

Must-Have Requirements

✓ Must be authorized to work in the U.S. without future visa sponsorship.

✓ Able to work onsite in San Francisco, CA five days per week.

✓ 3+ years in applied ML focused on ranking, recommendations, or search in production.

✓ Strong Python; experience with PyTorch or TensorFlow (Hugging Face a plus).

✓ Hands-on with embeddings and vector search (pgvector, FAISS, Pinecone, or Weaviate).

✓ Proven experience taking models from notebook to production: packaging, APIs, CI/CD, canary/rollback, monitoring.

✓ Data pipelines for training and evaluation (e.g., Airflow, Prefect, Dagster, or dbt) and sound data-quality checks.

Benefits

• Containers and orchestration experience (Docker, Kubernetes or ECS) and CI/CD pipelines you designed and ran.

• Proven ability to design and operate data pipelines and distributed systems for both batch and low-latency use cases.

• PostgreSQL at scale, ideally with pgvector/embeddings exposure for ML-adjacent workloads.

• Strong observability practices: metrics, tracing, alerting, incident management, and SLOs.

• Excellent collaboration with AI/ML and product teams; clear communication of tradeoffs and risk.

• Work authorization in the U.S. and willingness to be on-site five days a week in San Francisco.

• Experience supporting model training and inference pipelines, feature stores, or evaluation loops.

• Prior work with streaming voice, low-latency systems, or recommendation/retrieval stacks.