At a glance
Own Confido’s ML platform end to end, building the pipelines, cloud infrastructure, serving, and evaluation systems that make document AI, forecasting, and agentic workloads reliable in production.
Summarized by AI from the original posting
What you'll do
- Own ML pipelines from experimentation through production
- Build reproducible environments and prototype-to-production workflows
- Establish cloud infrastructure as code and CI/CD for ML systems
- Serve and optimize inference and forecasting workloads for latency, throughput, and cost
- Manage data interfaces between models, agents, and platform data systems
- Ensure reliability, observability, security, and privacy
- Measure model and agent quality through online evaluations and human-in-the-loop review
Requirements
- 015+ years in MLOps, ML platform, AI infrastructure, or platform engineering
- 02Production ML systems experience
- 03Strong Python and production application codebase experience
- 04Deep cloud infrastructure, distributed data systems, and Infrastructure as Code knowledge
- 05Experience building end-to-end ML pipelines
- 06Experience productionizing research/AI team outputs
- 07High ownership in a fast-moving startup
Perks
Skills
Full 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 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 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
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Own ML pipelines end to end — experimentation to production — and the infrastructure behind training, inference, and agentic workloads
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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
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Stand up the cloud foundation as Infrastructure as Code and the CI/CD that ships ML safely
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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)
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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
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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
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5+ years in MLOps, ML platform, AI infrastructure, or platform engineering — on production ML systems, not pipelines on paper
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You live at the seam of software and infrastructure: equally at home writing production code and standing up cloud infra.
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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
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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
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Strong Python and comfort in a production app codebase (Ruby, Java) monitoring, security, and cost are instincts, not afterthoughts
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High ownership in a fast-moving startup, and experience productionizing what research/AI teams build
Nice to have
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LLMOps tooling — tracing, prompt/version management, eval harnesses
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Inference optimization (vLLM, ONNX, TensorRT) and GPU / spot-instance economics
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ML platform and orchestration tooling (MLflow, BentoML, Ray, Airflow)
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Large-scale data systems (Snowflake, Kafka) and vector databases
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Managed ML services (Bedrock, SageMaker, Vertex AI)
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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
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Equity — own a piece of what you're building
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Fully paid health coverage with Aetna (we cover 100% of premiums)
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Top-tier dental and vision through Guardian
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12 weeks paid parental leave
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Unlimited PTO, plus regular 4-day holiday weekends we actually take
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401(k) through Vestwell
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Paid relocation — we'll get you here
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Full desk setup on day one (laptop, monitor, keyboard) + a $200 stipend to make it yours
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Catered Friday lunches, team dinners on us, and unlimited coffee + snacks featuring our own brands
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.
About Confido
Confido is a next-generation financial automation and intelligence platform that helps fast-growing CPG brands operate and scale more efficiently.
Compensation: $200K - $270K
Experience: 5+ years