Senior Machine Learning Engineer

Machine Learning Engineer - MLOps

Hybrid - NYC

We are partnering with a fast-growing healthcare AI startup that recently raised $55M in Series B funding and is scaling AI agents already reviewing real medical claims.

Their mission? Reduce waste in healthcare spending through smarter, more transparent claims operations, and help make care more affordable.

They’re hiring an MLOps Engineer to own the infrastructure, evaluation, and monitoring that keep these systems reliable as their customer base grows. You’ll report directly to the Head of AI and collaborate with AI engineers, and clinical experts, with meaningful ownership over how the team builds, ships, and measures production AI.

What you’ll own:

• Operate production LLM workloads using Temporal or Airflow and Kubernetes, managing retries, timeouts, rate limits, and provider fallbacks.
• Build agent evaluation covering quality, throughput, and cost, with release gates that catch regressions before deployment.
• Develop experiment tracking, production monitoring, and versioned pipelines for models, prompts, and agent configurations.
• Develop and fine-tune ML models and LLMs while improving inference performance and cost.

What they’re looking for:

• Around 4+ years of experience , including 2–3 years in dedicated MLOps work .
• Strong Python and hands-on experience operating LLM workloads in production.
• Experience owning evaluation for production AI agents.
• Practical model development, training, and fine-tuning experience.
• Familiarity with MLflow, Weights & Biases, or similar tracking and observability tools.

Experience with AWS Bedrock, Azure OpenAI, CI/CD, or healthcare systems would be a plus.

This is a chance to take substantial ownership at a funded startup, work closely with AI leadership, and build systems with a clear real-world purpose.

Applicants must have current U.S. work authorization; visa sponsorship is unavailable.