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About This Role
Staff / Principal MLOps Engineer
Contract (6 months, potential to convert) or Full\-Time \| USD $195,000 \- $220,000 (NYC) \| Remote (US or Canada)
Introduction
Come join our Data team!
High velocity, high intensity, high trust, high bar, high impact, and a will to win.
If those words resonate deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world.
At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI\-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.
Ready to make an impact? Apply today.
*Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10\. We encourage you to apply if you see a strong (not necessarily perfect) fit.*
The Opportunity
Your Role
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We're seeking a Staff / Principal MLOps Engineer to join our team. Our data and ML infrastructure has grown fast alongside the business, and it now needs senior ownership to bring it to where it should be. You will come in, assess the state of our pipelines and data architecture with clear eyes, decide what to fix and in what order, and then go fix it yourself. This is a role for someone senior enough to write systems designs grounded in what our customers actually need, and hands\-on enough to be in the codebase implementing them. We are open to running this as a six\-month contract or as a full\-time hire, depending on fit and what you are looking for.
What You'll Own
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- Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change and why.
- Systems design: Design data and ML systems that are anchored in customer needs and built to last, with clear tradeoffs documented so the team can build on them.
- Hands\-on implementation: Do the work: rebuild and harden pipelines, upgrade the data architecture, and ship the fixes yourself rather than handing off a deck.
- Reliability and standards: Raise the bar on observability, reliability, and data quality, establishing the patterns and practices the rest of the team can run with.
What You Bring \- Experiences, Skills, Education
Required Experience
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- Staff or principal\-level experience in MLOps, data engineering, or ML platform/infrastructure
- A track record of walking into complex, fast\-grown systems, diagnosing the real problems, and materially improving them
- Strong systems design ability: you can translate customer and product needs into durable, scalable architecture and communicatewrite it down clearly
- Genuinely hands\-on: you are as comfortable in the codebase implementing the fix as you are in the design doc
- Deep experience building and operating production data pipelines and ML workflows at scale
- Fluency with the modern data and ML stack and the cloud infrastructure it runs on
Bonus Points
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- Experience standing up MLOps practice (CI/CD for models, experiment tracking, feature stores, monitoring) from an early stage
- Background in mission\-driven, workforce, or government\-adjacent data environments
- Publications, presentations, blog posts, or other public artifacts showcasing your expertise and knowledge of best practices in MLOps
- Comfort mentoring and leveling up a small data and engineering team while you build
Our Tech Stack for Data
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- Languages: SQL, Python
- Data orchestration and transformation: Airflow, dbt
- Data storage and warehousing: PostgreSQL, Redshift, MongoDB (for unstructured data)
- Machine learning and experimentation: AWS SageMaker
- Visualization and reporting: Looker
- Infrastructure: AWS ecosystem (S3, Lambda, Glue, Redshift)
Your Education
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Your alma mater isn't our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head\-on, and know your strengths and gaps intimately, you're our person.
The Logistics \- Location, Compensation
Location
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\[CA/US Remote] We are open to candidates living anywhere in Canada or the US. For candidates living in Toronto, our office is conveniently located at 325 Front St West (a short walk from Union Station). For candidates living in New York City, our office is at 18 W 18th Street. You are welcome to come in on a hybrid schedule.
Travel Expectations
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Although this role is remote, you may be expected to travel up to once per quarter for offsites and team gatherings.
Compensation
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We are open to engaging this role as a six\-month contract with potential to convert, or as a full\-time hire. For the full\-time path, the base salary range is USD $195,000 to $245,000 for candidates based in New York and CAD $175,000 to $220,000 for candidates based in Toronto, benchmarked to the middle of the market for comparable venture\-backed companies. For the contract path, the rate range is USD $120 to $170 per hour, commensurate with level. The final figure reflects the varying levels of expertise and responsibilities that will be determined through the interview process, based on applied experience and other criteria established by the hiring committee.
The Hiring Journey
Hiring Journey
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At FutureFit AI, our hiring process is designed to help you assess whether this role and our culture are the right fit based on your unique skills, mindset, and experiences. We move fast and work with intensity, so we want you to get a real sense of that from the start.
Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
- Online Application
- Initial Screen with Director of People \& Culture
- Interview with Hiring Manager
- Performance Challenge
- Final 1:1 Interviews
- Final Decision
*Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances.*
Ready to shape the future of work?
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At FutureFit AI, we're not just building a company—we're transforming how talent and opportunity connect. Join our driven team united by a commitment to job seekers and the workforce ecosystems we serve.
Company Snapshot:
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- Team: 30\-50 across US and Canada (hubs in NYC and Toronto)
- Customers: Workforce development agencies and intermediaries, government agencies, employers
- Industry: SaaS/AI technology
- Funding: Bootstrapped 0\-1, then raised funding led by JP Morgan
- Structure: Growth, Customer Success, Product, Engineering, Data, People \& Culture, Finance \& Operations
Our Core Principles
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- Be Curious
- Drive to Outcomes
- Raise the Bar
- Speed Matters
- Own It
- We Over Me
Use of AI in Hiring
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At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment. We use AI in the following ways:
- Screening support: AI may help us compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input.
- Interview support: In some interviews, we may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation.
- Insights, not decisions: AI provides data points to support our team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people.
*We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request an accommodation.*
*FutureFit AI All rights reserved, we are proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics. We encourage people of different backgrounds, experiences, abilities, and perspectives to apply.*
Salary Context
This $249K-$353K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $190K across 22 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 3,823 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At FutureFit AI, 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 $217,200 based on 87 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($301K) sits 39% above the category median. Disclosed range: $249K to $353K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
FutureFit AI AI Hiring
FutureFit AI has 2 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer. Based in New York, NY, US. Compensation range: $155K - $353K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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