Interested in this AI/ML Engineer role at Deepgram?
Apply Now →About This Role
Location
------------
USA \| Remote
Employment Type
-------------------
Full time
Location Type
-----------------
Remote
Department
--------------
Product
Compensation
----------------
- San Francisco, NYC \& SeattleBase Salary Range $215K – $268K • Offers Equity • Offers Bonus
- Everywhere else in the U.S.Base Salary Range $200K – $250K • Offers Equity • Offers Bonus
This range is determined by work location and additional factors, including job\-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.
Please note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits.
Company Overview
====================
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
============================
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
The way developers find and adopt an API is changing. More and more, an AI coding agent discovers us, chooses the provider, writes the integration, and consumes our API, often with no human ever at the console. Deepgram is looking for a Staff Product Manager to own our product experience for that agent across its whole life with us: how an agent discovers and chooses Deepgram, how it integrates, how it uses the product in production, and how it verifies its own work. You will own that experience end to end, and you will build the system that keeps improving it as agent behavior changes. You report to the VP of Self\-Serve.
This is a product management role in the conventional sense: you own the product, its direction, and its decisions, and engineering builds it. Two things set the role apart, and both are required — you are a former engineer who still builds to think and to prove a point, and you are deeply AI\-native, with shipped work to show for it. You will prototype, read and write code, and reason with engineering at depth; your job is to own the product, not to be its implementing engineer.
What You'll Do
- Own the agent's experience of Deepgram across its lifecycle — discovery and recommendation, integration and onboarding, production use, and verification.
- Stand up a system that measures and optimizes every stage of the funnel for agents, and keep it current as agent behavior changes.
- Own the product surfaces specific to the agent experience: signup and authentication, the trial\-key and token defaults and programmatic key provisioning, console onboarding, and the verification tooling that lets an agent confirm its integration is actually correct.
- Set the requirements for what the agent experience needs from the shared developer platforms — SDK ergonomics, the agent\-readable documentation and llms.txt, the MCP server, the CLI, the skills package, and starter templates — and prototype the changes directly, in partnership with the team that owns those platforms.
- Stand up the operating system your work runs on — the rhythms of business, data\-driven optimization, and the experimentation platform — by building it in\-house or by researching and deploying the best tools available.
- Turn the scale of agent traffic into fast feedback loops, so the product improves as agents use it.
- Bring the product's point of view on agents as users: what they need, where they fail, and what to change, grounded in how models actually retrieve, choose, and integrate.
You'll Love This Role If You
- Were an engineer, moved to product to own outcomes, and never stopped building.
- Think like an architect and can design and stand up a self\-optimizing system across discovery, onboarding, and integration.
- Have felt, first\-hand, how an AI agent succeeds or fails at a real integration, and have strong opinions about why.
- Want to own a product that is becoming the front door of the business, at the moment it is becoming that.
- Are energized by being early — defining the practice, not inheriting it.
It's Important to Us That You Have
- Excellent product management judgment. You own product and roadmap, set direction, decide under uncertainty, ship outcomes, and lead cross\-functional work without authority. You can show the results.
- A former engineer's depth (required). You were a senior software engineer, or more, before you moved to product. You architect and ship production systems, you read and write real code, and you reason with engineering at their level. You are not a vibe coder who assembles what a tool generates.
- Deep AI fluency, proven by shipped work (required). You have personally built and shipped AI software that goes well beyond prompt files and markdown — agents, MCP servers, CLI tools, agent and evaluation harnesses, real model\-integrated tools — and it is public. Send us the GitHub; we will read the code, the commits, and the design.
- Proven ability to stand up a complete system from scratch — the rhythms of business, the reporting and optimization, the experimentation platform — yourself or in\-house, or by researching and deploying the right tools.
- PLG and developer\-product fluency. You understand how developers, and increasingly their agents, adopt APIs, and you understand product\-led growth.
- The judgment to distrust a number or a passing test before you build on it. You ask whether it is real, as a reflex.
- Clear communication with executives: you lead with the decision, keep your method in reserve, and hold up under pushback without either caving or digging in.
It Would Be Great If You Had
- Built specifically for AI agents as the consumer — MCP servers, agent harnesses, CLI tools, agent\-readable docs, tool definitions, or evals for agent output.
- Experience with voice, audio, or real\-time streaming systems.
- A track record of open\-source work with real adoption.
- Time in a company with both a self\-serve and an enterprise motion.
Compensation Range: $200K \- $268K
Salary Context
This $200K-$268K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($234K) sits 9% above the category median. Disclosed range: $200K to $268K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deepgram AI Hiring
Deepgram has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span San Francisco, CA, US, Remote, US. Compensation range: $235K - $268K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.