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About Distyl AI
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Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
We research and deploy technologies that power AI\-native operations — both for our partners and for Distyl itself. Our work spans research into self\-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission\-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.
Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board\-members of 20\+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.
What We Are Looking For
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As a Lead AI Strategist, you are the partner our largest customers turn to when they rethink how their business runs on AI. You work alongside CIOs, COOs, and their leadership teams on the technology and business strategy that reshapes how a company operates — and that partnership becomes some of Distyl’s deepest, fastest\-growing relationships.
These are companies with hundreds of thousands of employees and problems measured in the hundreds of millions of dollars. You learn their business better than almost anyone outside it, bring Distyl’s engineers and researchers to their hardest problems, and own the outcome — as a strategic partner, not a vendor. The relationships you build run into the tens of millions of dollars a year and grow from there. *The revenue is the result of trust, not a pitch.*
Key Responsibilities
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- Be the strategist in the room. Sit with CIOs, COOs, and their teams on where AI changes their business. Bring a point of view they can’t get from their own staff, and shape the technology and operating strategy — not a product pitch
- Understand the business cold. Learn how the customer makes money and where the friction is. Find the problems worth hundreds of millions to solve, and frame them in their language, not Distyl’s
- Turn strategy into outcomes. Translate executive conversations into real programs, mobilize Distyl’s forward\-deployed engineers and researchers, and stay accountable to the metric that has to move
- Grow the relationship. Expand from a first program into a portfolio across business units and budgets. Done well, these become Distyl’s largest relationships — tens of millions a year and climbing
- Earn trust over the long run. Make the call that serves the customer even when it costs Distyl in the short term. That is what keeps a relationship compounding
- Be the connective tissue. Align Distyl’s delivery, product, and research teams behind the commitments you make, and give leadership a clear read — explanations and plans, not excuses
Who You Are
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- A track record as a trusted advisor to senior executives — CIOs, COOs, and their peers — at large enterprises. The person leaders call before they’ve decided what to do
- 10\-15\+ years of experience in management consulting, an operational/ transformation leadership role within large enterprises, or product leadership role within enterprise technology companies
- Experience in standing up and delivering strategic agendas ($100M\+ value impact) with cross\-functional executive team \- shaping roadmaps, driving scoping and discovery, establishing ROI and value case for funding approvals
- Fluency in both technology and business strategy: you can hold a conversation on architecture and on the P\&L, and translate between the two
- Experience turning strategic relationships into large, multi\-year programs — and growing them
- Command of complex, multi\-stakeholder enterprise dynamics — procurement, security, legal, and competing internal agendas — without ever reading as a salesperson
- AI\-native instincts: you use frontier AI tools yourself and can speak credibly about what they change for a business
- The judgment to tell a customer a hard truth, including when not to buy. Serve, don’t sell.
- A history of leading large\-scale enterprise programs — transformations with significant budgets ($XXM\+) and many moving parts
- You’ve been the person an executive team trusts with their biggest technology\-driven bets
What We Offer
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- The base salary range for this role is $200 – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package
- 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible time off
- Retirement and financial planning benefits, including access to pre\-tax HSA, FSA, and commuter accounts, 401(k), and financial coaching resources
- Comprehensive wellness benefits, including physical fitness, mental well\-being, and fertility and family\-building benefits through Carrot
- Complimentary in\-office lunches and snacks provided
- Access to state\-of\-the\-art AI models, generous usage of modern AI tools, and real\-world business problems
- Ownership of high\-impact projects across top enterprises
- A mission\-driven, fast\-moving culture that values curiosity, pragmatism, and excellence
Distyl has offices in San Francisco and New York. This role follows a hybrid collaboration model with 3\+ days per week (Tuesday–Thursday) in‑office.
We believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected characteristic. We encourage candidates from all backgrounds to apply.
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Distyl AI, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $200K to $250K.
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.
Distyl AI AI Hiring
Distyl AI has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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).
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 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
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