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About This Role
Location: United States About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com.
About the role
Partner Manager, AI Ecosystem Partnerships · Turing Intelligence · United States (New York City preferred) · Full-time
Turing Intelligence builds and deploys AI systems for enterprises such as BlackRock, Apollo, PepsiCo, and Goldman Sachs. We run as a startup inside Turing, and our forward-deployed engineers work directly in client environments.
The foundation-model providers sell models and enterprise agreements, but they do not want to staff the implementation. Their customers still need someone to build real systems on those models, and that is our business. When a model provider recommends implementation partners into one of its enterprise accounts, we want Turing to be on that list.
We already have working relationships with two of the leading foundation-model providers. Both partners know us, but almost no revenue flows through either channel yet. We are hiring our first Partner Manager to change that: to sell Turing through and with these partners, run the partner programs behind that motion, and own the revenue that comes through the channel.
This is a hands-on, player-coach role. You will work on your own at first, and if the channel proves itself, you may build a team under it.
What you'll do
Sell through and with our partners
- Pitch Turing to the partners' account executives and field teams, so they recommend us into their enterprise accounts
- Build partner-sourced and partner-influenced pipeline by mapping partner account teams onto our target accounts and joining their account planning
- Co-sell in the field: joint account plans, shared pursuit calls, and joint proposals
- Route Turing-originated enterprise demand toward partner consumption, which earns referrals back to us
- Own the revenue that comes through the channel, and grow it quarter over quarter
Run the partner programs
- Manage each program end to end: onboarding, registrations and referrals, certifications, tier progression, partner credits, market development funds, and training
- Keep the paperwork and tracking tight and consistent, and own partner pipeline reporting as the single source of truth inside Turing
- Run the partner cadence: regular partner check-ins, quarterly business reviews, executive meetings, and partner events
- Keep our engineers certified and current on each partner's platform, and turn each model or platform launch into activity across our accounts
Treat competing partners fairly
- Manage partners who compete with each other, keeping each partner's information separate and giving none of them an advantage over another
- Raise conflicts early and honestly, including where a partner's own services team competes with us or where our delivery capacity cannot back a commitment we are asked to make
Work across Turing
- Run the channel much as a general manager runs a vertical, working mostly with our financial services (BFSI) business and with other verticals as accounts require
- Build the case studies and references partners ask for before they recommend us, starting from delivered work and new wins
- Partner with our delivery leaders to make sure every commitment made through the channel can be staffed and delivered
What we're looking for
Required
- 5+ years in partnerships, alliances, or channel, including at least 2 years carrying a quota or a partner-sourced revenue target
- Partner or alliance experience on the services side: you have managed a professional services or consulting firm's partnerships with cloud or technology vendors, ideally at a boutique or mid-size firm
- Both halves of the job: you have pitched your firm to a vendor's account teams and won referrals, and you have run a partner program's registrations, co-sell, tiers, and funding
- Fluency in the partner playbook: sourced versus influenced attribution, deal registration, co-sell, cloud marketplaces, and market development funds
- Discretion: you can manage partners who compete with each other and keep each relationship fair and separate
- Commercial judgment: you can read a P&L, model a referral structure against a margin floor, and tell revenue from profitable revenue
- Range: comfortable with a partner's field rep and a partner's executive, and with a delivery lead and a general manager inside Turing
- A player-coach who wants to do the work directly and build a channel from early stage
Preferred
- AI experience: partnerships with a foundation-model provider or AI platform, or selling AI implementation work
- Enough technical fluency to discuss model selection, agents, and deployment with an enterprise CTO or a partner's solutions architect
- Experience managing two or more competing vendor partnerships at the same time
- Experience selling into enterprise financial services
How you'll be measured
The first measure is revenue, and revenue growth, through the partner channel. Behind it sit partner-sourced pipeline, partner-influenced revenue and joint customer wins, gross margin on partner-sourced work, partner tier progression, and the number of certified engineers on each partner platform.
Compensation
The compensation range for this role is $240,000–$260,000.
Values
- We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
- We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
- We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
- Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
- Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
- Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.
- Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
- Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Salary Context
This $240K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At turing, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $240K to $260K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
turing AI Hiring
turing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $260K - $260K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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