Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
NYC Office · New York, United States
Open 4 months, first seen by us 125 days agoLocation type: On-siteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: Product & Technology › ML/AIFrom the employer’s posting
Do you require visa sponsorship to work legally in the United States (now or in the future)? This includes needing sponsorship for CPT, OPT or other visa types to work in the US.From the employer’s application form
Own ML pipelines from experimentation to production, including training and inference infrastructure
Build reproducible environments and fast paths so the AI/ML team can ship new models and agents
Set up cloud infrastructure as code and CI/CD for shipping ML safely
Serve and optimize inference and forecasting for latency, throughput and cost
Keep model and agent quality measurable in production through online evals and human review
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
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
keep model and agent quality measurable in production through online evals and human-in-the-loop review
Open 4 monthsWe first saw this posting 125 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Day 125
Up longer than most. Similar jobs usually come down between day 7 and day 72.
From 4,777 similar postings that came down. Coming down is not always a hire.
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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
- 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
- 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)
- 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
- Large-scale data systems (Snowflake, Kafka) and vector databases - Managed ML services (Bedrock, SageMaker, Vertex AI) - Multimodal or generative AI in production
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
The brands on your kitchen shelf are run by people doing extraordinary work that nobody sees.
We're building the platform they run on, and a brand they'll be proud to be part of.
Confido is the AI operating system that runs cash application, deductions, trade and planning in one place for CPG brands. That means Finance, Sales, and Ops finally work on the same number.
We’re trusted by 250+ brands managing $30B+ in revenue, including Daisy, OLIPOP, Simple Mills, Idahoan, and more. We've achieved best-in-class growth and recently raised a $55M Series B led by Insight Partners to accelerate our momentum.
If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.
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)
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
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
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.
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New York, United States
Working pattern and location restrictions need checking in the full posting.
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