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About This Role
Location: United States
About Turing
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Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high\-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission\-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.
Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com
About the role:
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Turing is looking for people with GenAI experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization leading a GenAI project end\-to\-end from POC to full\-scale implementation and you will be leading a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry\-leading multi\-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises.
Role responsibilities
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- Client Relationship and Communication:
- + Lead discussions with clients and prospects to build AI product pipeline and expand to more processes.
+ Prioritize client engagement, strengthen relationships, and manage expectations proactively.
+ Act as a point of contact for client communication, feedback and escalations.
+ Evaluate and select use\-cases for new product development and unlock new revenue opportunities for the client
+ Participate in client’s internal stakeholder meetings (when applicable) to capture, clarify, and consolidate requirements across functions, ensuring all inputs are properly documented and mapped into actionable product needs.
- Tech Leadership:
- + Provide technical direction to the engineering team on appropriate solutioning \& system design for a given problem statement
+ Align the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction.
+ Study new LLM research topics and engage in conversation on same with client stakeholders to showcase technical prowess and build value for Turing.
- Team Leadership and Coordination:
- + Drive cross\-functional alignment across engineering, product, and client success teams.
+ Remove blockers for both the client \& internal teams by ensuring smooth communication and effective prioritization flows.
+ Conduct regular 1:1 meetings focused on support, expectations, delivery alignment, and general well\-being.
- Project Planning and Oversight:
+ Manage the big\-picture program timeline (releases, phases, go\-live plans) using engineering velocity and capacity inputs provided by the Engagement Manager.
+ Co\-own delivery planning by managing the program\-level timeline and facilitating Sprint Planning sessions ensuring release milestones and sprint goals are aligned with business priorities, clearly defined requirements, and the engineering capacity and feasibility inputs provided by the Engagement Manager.
- + Own overall delivery to ensure quality, scope, and timelines are consistently met.
+ Ensure robust business\-facing documentation, including requirements documents, BRDs/PRDs, implementation plans, product roadmaps, and client\-facing marketing or enablement materials.
+ Ensure delivery decisions reflect an understanding of operational costs, ROI, and long\-term business impact.
+ Identify delivery risks, create proactive mitigation plans, and track program health across all milestones with keen insight.
+ Acknowledge new client requests promptly and partner with the Engagement Manager to assess feasibility, capacity, and timeline impact before providing any commitment to the client.
Required skills
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- Excellent communication skills \& stakeholder management to effectively collaborate with client SMEs
- Entrepreneurial \& founders mindset to own end\-to\-end GenAI project deliveries and guide customers through various phases
- 10\+ years of professional experience with at least 4\+ years in driving Machine Learning \& AI projects
- Good understanding of the latest GenAI agentic designs and frameworks like Langchain, Langgraph, etc.
- Understanding of Cloud services including Azure, GCP, or AWS
- Understanding of Agile methodologies and project management tools like JIRA, Confluence.
### Compensation Range: $240k \- $260K
Values
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- 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
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- Amazing work culture (Super collaborative \& supportive work environment; 5 days a week)
- Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
- Competitive compensation
- Flexible working hours
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. 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: $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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($250K) sits 14% above the category median. Disclosed range: $240K to $260K.
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.
turing AI Hiring
turing has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, US. Compensation range: $245K - $260K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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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