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Location
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USA \| Remote
Employment Type
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Full time
Location Type
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Remote
Department
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Engineering
Company Overview
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Deepgram is the leading platform underpinning the emerging trillion\-dollar Voice AI economy, providing real\-time APIs for speech\-to\-text (STT), text\-to\-speech (TTS), and building production\-grade voice agents at scale. More than 200,000 developers and 1,300\+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice\-native foundation models are accessed through cloud APIs or as self\-hosted and on\-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.
Company Operating Rhythm
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At Deepgram, we expect an AI\-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.
Additionally, we move at the pace of AI. Change is rapid, and you can expect your day\-to\-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9\-to\-5\.
The Opportunity
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Deepgram's ambition is to build a generational company with a small, exceptional team — which only works if every engineer and every function operates with serious AI leverage. We're looking for a Head of AI Enablement Engineering to own that mission end to end: making Deepgram one of the most AI\-native companies in the world, in practice and not just in principle.
This is a build\-first leadership role, not a steward or training role. You'll personally evaluate tools, build the agents and workflows that show what great looks like, and set the standards that the rest of the company adopts. You'll turn our AI\-native strategy into shipped capability — reusable agents and skills, MCP integrations, paved\-road workflows, and the enablement hub and patterns that let any team go from idea to working tool fast and safely. You'll partner closely with Engineering, Platform/Internal Tools, People Ops, and functional leaders across the company, and you'll be measured on real outcomes: adoption, productivity, and the quality of what people build.
You'll lead largely through building and influence, with the runway to grow a small team and a network of champions as the function scales. It's a high\-visibility seat with executive sponsorship and a mandate to set direction where there is no established playbook.
What You'll Do
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- Own and drive AI enablement engineering across Deepgram — the strategy, the standards, and the hands\-on building that make AI leverage real in every function.
- Personally evaluate, prototype with, and make the calls on the AI tools, agents, models, and orchestration layers Deepgram adopts; avoid tool sprawl and make pragmatic build\-vs\-buy decisions.
- Build the reference implementations: reusable agents and skills, MCP servers, paved\-road workflows, prompt and pattern libraries, and the enablement hub where the best internally\-built tools are surfaced and elevated.
- Set and run the company\-wide AI adoption strategy — the metrics, milestones, and reporting cadence leadership uses to track progress, framed around measurable productivity and quality, not activity.
- Partner with Platform/Internal Tools, Security, and Data to define guardrails that are embedded into platforms rather than enforced through gates — safe\-use patterns, access, and data handling that make adoption easier, not harder.
- Build and lead a distributed champions network embedded in teams, and grow a small central team over time as impact scales.
- Partner with People Ops on AI\-native onboarding and fluency, so new and existing teammates do real reps inside the tools and leave the system better than they found it.
- Stay ahead of a fast\-moving landscape and translate emerging AI capabilities into pragmatic, Deepgram\-ready practice.
You'll Love This Role If You
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- Want to define how an entire company works with AI — and you'd rather build the proof than write the memo.
- Are energized by ambiguity and a blank page, and you set direction where there's no playbook yet.
- Are hands\-on and current: you build agents and workflows yourself and can sit across from senior engineers as a peer on day one.
- Care about real outcomes — adoption, time saved, quality — not vanity metrics or shelf\-ware.
- Like operating across an org, bringing skeptical teams along through demonstrated value rather than mandate.
- Believe a small, AI\-leveraged team can outbuild a much larger one.
It's Important To Us That You Have
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- A strong engineering background with the hands\-on ability to build production\-quality agents, tools, and automations yourself.
- Deep, current fluency with the modern AI tooling landscape — coding agents, LLM application patterns, prompting, retrieval, MCP/agent tooling, and orchestration.
- A track record of driving technology adoption and changing how people work at scale, in environments that didn't start out asking for it.
- The ability to operate across business and technical functions and influence without direct authority, including credibility with senior engineering leaders.
- Strong product and platform instincts — you treat enablement as a product, with users, adoption, and a roadmap.
- Excellent communication — you can demo, document, evangelize, and report outcomes to executives in plain language.
- Comfort defining safe\-use guardrails and data\-handling practices in partnership with Security and Platform.
It Would Be Great if You Had
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- Experience standing up an AI enablement, developer productivity, or engineering effectiveness function from scratch.
- Background building internal platforms or developer\-facing tooling that engineers actually adopted.
- Experience leading a small team and/or a distributed champions/center\-of\-excellence model.
- Familiarity with enterprise AI search and knowledge tooling (e.g., Glean, Notion AI) and agent orchestration frameworks.
- A point of view on measuring developer productivity and AI impact, with the nuance that entails.
- Experience in a fast\-moving, AI\-native engineering organization.
Salary Context
This $160K-$220K range is above the median 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 Deepgram, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $160K to $220K.
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.
Deepgram AI Hiring
Deepgram has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $274K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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