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About This Role
About Fuel Cycle :
Fuel Cycle empowers leading organizations with agile research solutions that deliver decision-ready insights — fast, flexible, and fully integrated. As a market research disruptor, our AI-powered Insights Platform is built for speed, precision, and scale. With cutting-edge tools and seamless audience connectivity, we help brands ditch the guesswork and make smarter, customer-led decisions at lightning speed.
Why work at Fuel Cycle?
Join a high-growth team where curiosity is valued, ownership is encouraged, and your work drives real-world impact. Whether you’re based at our Los Angeles HQ, New York City hub, working remotely across the U.S., or part of our global team in India, you’ll help shape the future of decision intelligence for some of the world’s most iconic brands.
The Role:
The AI Operations Associate is Fuel Cycle's internal AI transformation role — accountable for making every company function AI-native, starting with Sales and Marketing. This role begins by owning the GTM operations stack (Salesforce, marketing automation, pipeline data) and the AI workflows that sit on top of it, then expands scope to Finance and the broader internal operations surface as coverage deepens.
This is not a Revenue Operations seat with AI bolted on, and it is not an IT or process-documentation role. It is the person accountable for replacing manual, human-routed workflows with instrumented AI pipelines — across every internal function — and for measuring the result. When a workflow can be replaced by an agent, you build the replacement. When it can't yet, you fix the data and the process until it can.
The initial focus is GTM (Sales and Marketing), with Finance as a close collaborative partner from day one. Over time, scope extends to HR, Legal, Customer Success, and other internal functions as the playbook matures.
Key Responsibilities:
GTM Operations (Initial Focus)
- Own the operational tooling — Salesforce, marketing automation platforms, and connected data sources — including configuration, data hygiene, and integrations.
- Design and ship AI-native workflows across the revenue lifecycle: lead scoring, opportunity enrichment, forecast rollups, marketing attribution, and pipeline health monitoring.
- Build and maintain BI dashboards that give Sales and Marketing leaders real-time visibility into pipeline health, forecast accuracy, campaign performance, and funnel conversion.
- Deploy AI agents into the daily workflows of Sales and Marketing leaders so they can query, act, and decide without waiting on manual pulls or analyst turnaround.
Finance Collaboration
- Partner with Finance from day one on the data and systems that underpin the close cycle, forecasting, and budget rollups — ensuring a shared, trusted source of truth.
- Support Finance in identifying and automating repetitive workflows: month-end close preparation, variance analysis inputs, quote-to-cash process steps, and forecast consolidation.
- Serve as the systems and data authority for the annual planning cycle — territory design, quota setting, budget rollups, and forecast cadences — in close coordination with Finance leadership.
- Build the connective tissue between GTM systems and Finance systems so revenue data flows cleanly, without manual reconciliation.
Broader Internal Operations (Expanding Scope)
- Identify and prioritize AI automation opportunities across all internal functions — HR, Legal, Customer Success, IT, and others — as GTM coverage matures.
- Establish and own the internal AI operations playbook: how Fuel Cycle evaluates, deploys, and measures AI-native replacements for manual workflows company-wide.
- Champion AI fluency across internal teams — helping department leaders understand what is automatable, what isn't yet, and what the roadmap looks like.
- Partner with Product & Engineering on the underlying platform and MCP infrastructure that powers internal agent surfaces.
Systems, Data & Measurement
- Own the "single source of truth" standard across all functions you support — ensuring data is clean, definitions are agreed, and escalations over numbers are rare.
- Instrument every AI workflow you deploy with evaluation loops so quality, accuracy, and reliability are continuously measured.
- Maintain a clear, quantified view of manual hours displaced, workflow coverage, and time-to-answer improvement — and report it to leadership.
Your Success Metrics
- AI-Native Coverage — Percentage of routine workflows across supported functions executed or materially assisted by AI agents.
- Manual Hours Displaced — Dollar-equivalent time returned to GTM, Finance, and other operational leaders each quarter.
- Speed to Answer — Time from a leader's question to a trustworthy answer, whether via dashboard, agent response, or ad hoc analysis.
- Data Trust — Reduction in escalations and disputes over pipeline, attribution, revenue, and operational numbers.
- Function Coverage Expansion — Number of internal functions with at least one AI-native workflow deployed and measured.
Who You'll Work With
- Chief Product Officer / Chief AI Officer
- CRO and Sales leadership
- CMO and Marketing leadership
- CFO and Finance leadership
- HR, Legal, and other functional leaders (as scope expands)
- The existing AI Operations Associate (peer, focused on customer-facing teams)
- Product & Engineering partners who own the underlying platform and MCP infrastructure
Core Skills, Competencies & Attributes
- Strong systems thinker — able to hold Salesforce architecture, a marketing automation platform, a finance stack, and other internal tooling in view simultaneously and see the seams between them.
- Fluent in AI-native workflow concepts: agents, MCP, prompt engineering, evaluation loops, and human-in-the-loop design.
- Comfortable owning the "single source of truth" burden across multiple functions with competing priorities.
- Communicates clearly with both technical (engineering) and commercial (sales, marketing, finance, operations) audiences.
- Bias to build and ship — will prototype the replacement rather than document the current state.
Required Experience & Technical Skills
- Track record of building and shipping solutions with AI in a business context.
- Hands-on experience with agentic development: Claude Code, MCP, prompt engineering, evaluation loops.
- Python for data analysis and automation; SQL for querying and reporting.
- Familiarity with enterprise operational systems (CRM, marketing automation, ERP, BI) — enough to build against them, with willingness to become fluent across the stack.
- Comfort with no/low-code prototyping tools (Zapier, Retool, Superblocks) as the situation calls for them.
- Comfort with ambiguity and strong prioritization instincts across a broad operational scope. Fuel Cycle is building AI‑native internal operations powered by rapid, modular, goal‑driven agent workflows. The AI Operations Associate invents, prototypes, and deploys systems that automate end‑to‑end business processes— from onboarding and finance ops to customer support and research delivery.
Benefits & Perks:
Fuel Cycle is committed to supporting the well-being, flexibility, and growth of our team. We offer a competitive and inclusive benefits package that includes:
- Comprehensive Health Coverage : Medical, dental, and vision insurance plans
- 401(k) with Company Match : Plan for your future with our retirement savings program
- Equity Purchase Option : Participate in Fuel Cycle’s long-term success
- Flexible Work Schedule : Empowering you to balance life and work
- Generous Time Off :
+ 15 vacation days and 7 sick days per year + 12 company holidays + 4 floating holidays/recharge days to rest or celebrate what matters to you
- Paid Parental Leave : Time to bond with your growing family
- Monthly Internet & Phone Stipend : Support for remote work setup
- Wellness & Lifestyle Perks : Access to tools like Rightway (healthcare navigation), Headspace (mental wellness), Peloton (fitness) , and more
- Team Connection Perks :
+ Weekly community lunches , refreshments, and snacks at our LA & NY headquarters
Compensation Overview:
The expected starting salary range for this position is $85,000 - $105,000. This range represents the typical starting compensation offered to candidates hired into this role. Final base salary will be determined based on a variety of factors, including location, work experience, skills, knowledge, education, and certifications.
Commitment to Diversity, Equity, and Equal Opportunity:
At Fuel Cycle, we embrace the values of diversity, equity, and inclusion and are committed to fostering an inclusive company culture. We believe that everyone, regardless of their background or identity, should have equal access to opportunities for growth and advancement. Our selection processes and career pathways are designed to be fair, transparent, and free from bias. We value the unique perspectives and contributions of each team member, knowing that this diverse range of experiences strengthens our team. Fuel Cycle stands firmly against discrimination based on disability, sex, race, gender identity, sexual orientation, religion, national origin, age, veteran status, or any other protected status under the law.
Fuel Cycle is an equal opportunity employer and fully comply with the Americans with Disabilities Act (ADA). We will provide reasonable accommodations for qualified applicants and employees with disabilities, as needed, to enable them to perform the essential functions of their job and participate in the application and interview process. If you require accommodations during any part of the application process, please contact us at [email protected] to discuss your needs.
#li-hybrid
Salary Context
This $85K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fuel Cycle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $85K to $105K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Fuel Cycle AI Hiring
Fuel Cycle has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $105K - $105K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,340 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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