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 RhythmAt 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 OpportunityDeepgram 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 DoDefine 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.
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.
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).
