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About This Role
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
Compensation
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- Estimated Base Salary $180K – $240K • Offers Equity • Offers Bonus • 10% Annual 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
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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 is looking for a Senior Software Engineer \- Model Evaluation \& AI Systems to join the team responsible for validating the quality of our speech, audio, and multilingual models before they reach customers.
This team owns the evaluation and quality assurance surfaces that ensure Deepgram's models — across speech\-to\-text, text\-to\-speech, and increasingly LLM\- and multimodal\-powered systems — meet their performance targets in both batch and streaming environments. We build the pipelines, harnesses, canaries, and test frameworks that catch regressions, hallucinations, and quality issues before they impact customers, and we partner closely with Research to turn model expectations into automated, reproducible, enforceable checks.
In this role, you'll define evaluation methodology and build the infrastructure that measures model quality at scale. You'll create evaluation pipelines, define pass/fail criteria grounded in Research benchmarks, and build the monitoring that keeps models honest in production. Your work provides the trusted signals that inform release and optimization decisions, and directly protects the customer experience.
We're looking for a strong engineer who is equally comfortable building test infrastructure and reasoning about model behavior. This role is aimed at senior engineers with broad instincts for quality, measurement, and automation; hands\-on experience evaluating modern AI systems is a strong plus.
What You'll Do
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- Define and build evaluation methodologies for Deepgram's models, spanning speech\-to\-text, text\-to\-speech, and emerging LLM, RAG, agent, and multimodal systems.
- Design, build, and maintain automated evaluation pipelines across batch and streaming (e.g. WER, runaway/hallucination detection, latency and time\-to\-first\-byte), with a focus on correctness, reproducibility, and ease of adoption.
- Build scalable, reproducible evaluation infrastructure — harnesses, orchestration, and result\-aggregation pipelines — running against production models and, where needed, large GPU clusters.
- Translate Research benchmarks and expected model metrics into automated, enforceable pass/fail gates.
- Build and operate canaries and continuous\-monitoring systems that detect quality regressions in production before they reach customers.
- Partner with DevOps/Infra to stand up ephemeral test environments and results\-aggregation infrastructure.
- Work alongside Research, model training, inference, and product teams to provide trusted evaluation signals that inform release and optimization decisions.
- Integrate evaluation and quality gates into CI/CD so quality is verified continuously, not manually.
- Help raise the bar through code reviews, technical design discussions, and strong engineering and QA practices.
What We're Looking For
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- BS, MS, or PhD in Computer Science, AI, Applied Math, or a related field, or equivalent experience.
- 5\+ years of professional software or QA engineering experience, with a track record of shipping test infrastructure or evaluation systems (senior candidates with significantly deeper experience welcome).
- Solid backend/scripting experience in a language such as Python, Rust, Go, or similar.
- Experience designing and building automated test pipelines, evaluation frameworks, or data\-processing systems.
- Strong analytical skills and comfort reasoning about metrics, thresholds, and statistical variation in results — able to distinguish real regressions from noise.
- Ability to take charge of ambiguous technical challenges and communicate effectively across research, engineering, and product teams.
Nice to Have / Ways to Stand Out
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- Hands\-on experience evaluating modern AI systems such as LLMs, RAG pipelines, agents, or multimodal models, including model behavior analysis.
- Experience with React Native or other cross\-platform mobile frameworks for building tooling that's accessible beyond the desktop.
- Experience building or improving evaluation frameworks, benchmarks, or ML infrastructure used by other teams or external users.
- A strong appreciation for evaluation quality — correctness, reproducibility, and consistency across environments.
- Experience with voice, audio, speech recognition, or real\-time systems, and familiarity with metrics like WER, MOS, or latency/TTFB.
- Prior involvement in open\-source projects, through contributions, reviews, maintenance, or community engagement.
- Experience acting as a technical bridge across teams or platforms (evaluation, training, inference, agent frameworks), combining architectural understanding with clear communication and influence.
- Familiarity with cloud infrastructure, containerized/ephemeral environments, and monitoring tooling (e.g. Grafana, canaries, anomaly detection).
Compensation Range: $180K \- $240K
Salary Context
This $180K-$240K range is above the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Deepgram, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $180K to $240K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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
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