Senior ML Ops Engineer

United States Digital Space LLC; New York (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Fully paid health coverage

Dental and vision

Parental leave (12 weeks)

Unlimited PTO

401(k) through Vestwell

Relocation assistance

Desk setup on day one

Catered Friday lunches

Snacks and coffee

Job summary

United States Digital Space LLC in New York City seeks the first dedicated owner of its ML platform. You’ll own ML pipelines, the infrastructure behind training, inference, and agent workloads, and the cloud foundation that makes production scalable and cost-efficient.

Relocation is supported. You will stand up reproducible environments, deploy IaC, and partner with AI teams to ship reliable production systems while prioritizing security, privacy, and observability.

Qualifications

• 5+ years in MLOps or AI infrastructure on production systems.
• End-to-end pipeline ownership with secure, cost-aware infra.
• Strong Python and production app codebase experience (Ruby/Java as fallback).

Responsibilities

• Own ML pipelines end to end from experimentation to production.
• Provide reproducible environments and fast paths from prototype to production.
• Stand up cloud foundation as Infrastructure as Code and CI/CD for ML.
• Serve and optimize inference and forecasting workloads for latency and cost.
• Ensure reliability, observability, security, and privacy in production.

Skills

MLOps

Platform engineering

Cloud infrastructure

Python

Production systems

Ownership

Tools

Terraform

Kubernetes

AWS

GitHub Actions

Snowflake

Kafka

Job description

the company is the AI infrastructure powering modern CPG — the platform that 200+ brands like OLIPOP, Simple Mills, Dr. Squatch, and Tropicana use to run everything from deductions to production planning. Finance, accounting, sales, and operations, unified in one system for the first time.

We're growing 5x year over year and recently raised a $15M Series A led by Footwork and Y Combinator. We're a small, in-person team in New York City, which means the people who join now shape the product, the culture, and the company itself.

If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.

The Role

Be the first dedicated owner of the company's ML platform. Our AI/ML team already ships document-understanding, forecasting, and agentic systems into production — on infrastructure we've stood up by hand. You'll own that layer: the pipelines, serving, and cloud foundation that turn models and agents into reliable, cost-efficient production systems, at the scale of hundreds of thousands of documents and heavy LLM/VLM workloads.

Location: New York, NY (Relocation supported)

What you'll do

Own ML pipelines end to end — experimentation to production — and the infrastructure behind training, inference, and agentic workloadsGive the AI/ML team a paved road: reproducible environments and fast paths from prototype to production, so they can try new models and agents without fighting the infraStand up the cloud foundation as Infrastructure as Code and the CI/CD that ships ML safelyServe and optimize inference and forecasting workloads — latency, throughput, and cost — and the data streams feeding them (e.g. turning a heavy synchronous model call into an async, parallelized one)Own the data interface with data engineering: serve the right data to models and agents, and write their outputs back into the platform's data systems for the rest of the company to useMake reliability, observability, security, and privacy the default — and keep model and agent quality measurable in production through online evals and human-in-the-loop review, not just uptime

What we're looking for

Required

• 5+ years in MLOps, ML platform, AI infrastructure, or platform engineering — on production ML systems, not pipelines on paper
• You live at the seam of software and infrastructure: equally at home writing production code and standing up cloud infra.
• You've driven a real pipeline end to end and can walk through it: the architecture, the security and cost trade-offs, and what you'd change
• Deep cloud infrastructure understanding, distributed data systems, and IaC — you can boot an environment from scratch, wire CI/CD, and run containerized workloads in production without hand-holding
• Strong Python and comfort in a production app codebase (Ruby, Java) monitoring, security, and cost are instincts, not afterthoughts
• High ownership in a fast-moving startup, and experience productionizing what research/AI teams build

Nice to have

• LLMOps tooling — tracing, prompt/version management, eval harnesses
• Inference optimization (vLLM, ONNX, TensorRT) and GPU / spot-instance economics
• ML platform and orchestration tooling (MLflow, BentoML, Ray, Airflow)
• Large-scale data systems (Snowflake, Kafka) and vector databases
• Managed ML services (Bedrock, SageMaker, Vertex AI)
• Multimodal or generative AI in production

Our stack: Python · Ruby/Rails · AWS · Terraform · Kubernetes · GitHub Actions · Snowflake · Aurora/RDS · Redis · Kafka — with more of the above added as we scale.

Learn more about AI @ the company here

Perks + Benefits

• Equity — own a piece of what you're building
• Fully paid health coverage with Aetna (we cover 100% of premiums)
• Top-tier dental and vision through Guardian
• 12 weeks paid parental leave
• Unlimited PTO, plus regular 4-day holiday weekends we actually take
• 401(k) through Vestwell
• Paid relocation — we'll get you here
• Full desk setup on day one (laptop, monitor, keyboard) + a $200 stipend to make it yours
• Catered Friday lunches, team dinners on us, and unlimited coffee + snacks featuring our own brands

the company provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.