ML Engineer

Job Title:

ML Engineer What You Will Own

• End‑to‑End ML Lifecycle across real products: data ingestion, feature design, model selection, training, deployment, monitoring and iteration. No handoffs.
• Production‑grade ML systems built with PyTorch or Tensor Flow, focusing on latency, reliability, cost and failure modes.
• Applied GenAI and LLM work that creates measurable value: fine‑tuning, RAG, prompt orchestration, evaluation and guardrails. No hype‑first work.
• MLOps foundations: model versioning, CI/CD, automated testing, deployment pipelines, serving layers, monitoring and A/B experimentation.
• Tight partnership with product engineering and data teams. Translate fuzzy business problems into tractable ML solutions and quantify impact.
• Technical leadership: code reviews, model reviews, mentoring and raising the bar for the ML engineering discipline.
• Incident ownership: debug production failures, data drift, performance regressions and bias issues calmly and decisively.

• 7+ years of hands‑on ML engineering with clear senior‑level ownership of production systems.
• Strong academic grounding or equivalent applied depth in machine learning, computer science or related fields.
• Expert Python, deep familiarity with PyTorch (preferred) and Tensor Flow (acceptable).
• Demonstrated experience deploying, maintaining and scaling ML models in production environments.
• Solid cloud experience across AWS, GCP or Azure. Comfortable with Spark, SQL, Docker and Kubernetes.
• Strong grasp of ML fundamentals: model architectures, optimisation trade‑offs, evaluation, design, experimentation and rigor.
• Clear written and verbal communication. Able to explain complex systems without theatrics.

• Direct experience with LLM systems in production: fine‑tuning, RAG, evaluation, safety and cost control.
• Exposure to MLOps platforms such as MLflow, Kubeflow, Airflow or equivalent internal systems.
• Depth in one or more domains such as NLP, search, recommendations, forecasting or anomaly detection.
• Evidence of technical leadership: open‑source contributions, internal platform development, publications or scaled internal tools.

• Meaningful ownership over core AI systems, not edge experiments.
• Compensation aligned to senior impact, not titles.
• Performance bonus in the 10–20% range plus modest equity aligned to company stage.
• Full benefits including health, dental, vision, 401(k) and unlimited PTO.
• Learning budget and a hybrid Bay Area setup optimized for collaboration without dogma.
• Work that compounds: systems that ship, problems that matter.