AI/ML Engineer

AI/ML Engineer (AI Voice & Social Product) - w/ EquityLocation: San Francisco, CA (Onsite 5 days a week)Type: Full-Time Job OverviewOwn ML systems for a voice-AI product driving accurate match-making and continuous improvement. Collaborate on data pipelines, model training, evaluation, and deployment with emphasis on efficiency and low-latency. Key responsibilities: Design multi-stage retrieval and re-ranking for personalization; manage data pipelines ensuring reproducibility; train and fine-tune LLMs; run offline and online evaluations; set latency and cost targets for inference services.Required skills: 10+ years in … ResponsibilitiesKnown 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. Key Responsibilities• 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. QualificationsWe 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. Nice to Have• Experience supporting model training and inference pipelines, feature stores, or evaluation loops.• Prior work with streaming voice, low-latency systems, or recommendation/retrieval stacks.