JOB-1197

Target Salary: $130,000–$210,000 + Equity (0.05%–0.2%) + Benefits

JOB SUMMARY:

A rapidly growing defense technology company is seeking an ML Ops Engineer to design and own the infrastructure that enables ML teams to ship models faster and more reliably. The role is ideal for engineers who have built ML Ops platforms from scratch and want full ownership of end-to-end tooling in a high-growth, mission-driven environment. This is a full-time, on-site position in NYC (Flatiron).

ESSENTIAL DUTIES:

  • Automate model release & rollback with one-click CI/CD from training artifact to deployed model.

  • Maintain MLflow (or equivalent tools) for experiment tracking, model registry, and production deployment.

  • Design and implement robust data catalog & metadata layers to support discoverability and reproducibility.

  • Deliver SDKs, templated repos, and documentation to accelerate onboarding and productivity.

  • Enforce best practices, proactively identify bottlenecks, and align tooling roadmap with product goals.

OTHER DUTIES:

  • Collaborate closely with ML researchers to ensure rapid iteration and high-quality releases.

  • Support infrastructure scaling as the company grows.

  • Contribute to technical standards and engineering culture.

QUALIFICATIONS:

  • 2–7 years of ML Ops or developer infrastructure experience.

  • Proven background building platforms that package, track, and ship ML models into production.

  • U.S. citizenship required; must be eligible for security clearance.

  • Must be willing to work 5 days per week in the NYC office.

TECHNICAL SKILLS:

  • Proficiency in Python; comfortable with C++ or lower-level languages when required.

  • Strong AWS expertise (Networking, S3, Lambda/ECS) and IaC (CDK).

  • Fluency with experiment-tracking & model registry tools (MLflow, W&B, SageMaker, etc.).

  • Experience designing schemas and APIs for data discoverability and auditing.

  • Strong automation mindset – turning manual workflows into pipelines.

SOFT SKILLS:

  • Highly organized with strong documentation discipline.

  • Clear communicator with a collaborative approach.

  • Excited about building tools and platforms that accelerate ML teams.

PREFERRED TRAITS:

  • Startup experience owning infra/tooling from scratch.

  • Genuine enthusiasm for MLOps over pure modeling work.