Interested in this MLOps Engineer role at CONFIDO?
Apply Now →Skills & Technologies
About This Role
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
------------------
- 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 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
*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.*
Compensation Range: $210K \- $300K
Salary Context
This $210K-$300K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $177K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At CONFIDO, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($255K) sits 16% above the category median. Disclosed range: $210K to $300K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CONFIDO AI Hiring
CONFIDO has 1 open AI role right now. They're hiring across MLOps Engineer. Based in New York, NY, US. Compensation range: $300K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.