Senior ML Ops Engineer

Confido Health; New York (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Health coverage (Aetna)

Dental + vision coverage

Fertility & family support

Parental leave

Unlimited PTO

401(k)

Relocation support

Workspace stipend

Team lunches & snacks

Job summary

Confido Health is seeking the first dedicated owner of its ML platform in New York City. You will manage ML pipelines, training and inference infrastructure, and cloud foundations to support hundreds of thousands of documents and heavy workloads.

You will ensure reliability, observability, security, and privacy by default, enabling the AI/ML team to move from prototype to production with reproducible environments and scalable infrastructure.

Qualifications

• 5+ years in MLOps, ML platform, AI infrastructure, or platform engineering on production ML systems.

Responsibilities

• Own ML pipelines end to end from experimentation to production.

Skills

ML platform

MLOps

Cloud infrastructure

Python

Production code (Ruby/Java)

Ownership

Tools

AWS

Terraform

Kubernetes

Snowflake

Kafka

Airflow

MLflow/BentoML

Job description

Confido 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 with a small team in New York City; the people who join now will 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 Confido'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 workloads

• Give 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 infra

• Stand up the cloud foundation as Infrastructure as Code and the CI/CD that ships ML safely

• Serve 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 Confido to use

• Make 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 @ Confido here

Perks + Benefits

• Equity — own a meaningful piece of the company you're helping build

• Fully paid health coverage through Aetna — we cover 100% of employee premiums

• Top-tier dental and vision coverage through Guardian

• Carrot Fertility Pro — comprehensive fertility and family-forming support

• 12 weeks of paid parental leave

• Unlimited PTO — plus regular 4-day holiday weekends we actually take

• 401(k) through Vestwell

• Paid relocation support — we'll help you make the move to NYC

• Fully equipped workspace from day one — laptop, monitor, keyboard, and a $200 stipend to personalize your setup

• Team perks — catered Friday lunches, team dinners, and unlimited coffee + snacks featuring products from the brands we work with

Confido 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.