Senior Software Engineer - Model Evaluation & AI Systems

Deepgram • Remote, CA

8d • $180,000 - $240,000

About The Position

Deepgram is seeking a Senior Software Engineer - Model Evaluation & AI Systems to join a team focused on validating the quality of their speech, audio, and multilingual models before customer release. This team is responsible for the evaluation and quality assurance processes that ensure Deepgram's models, including speech-to-text, text-to-speech, and increasingly LLM- and multimodal-powered systems, meet performance targets in both batch and streaming environments. The role involves building pipelines, harnesses, canaries, and test frameworks to detect regressions, hallucinations, and quality issues, and collaborating with Research to translate model expectations into automated, reproducible checks. The engineer will define evaluation methodology, build infrastructure for large-scale model quality measurement, create evaluation pipelines, establish pass/fail criteria based on Research benchmarks, and develop monitoring systems to ensure model integrity in production. This position directly impacts customer experience by providing trusted signals for release and optimization decisions. The ideal candidate is a strong engineer comfortable with building test infrastructure and analyzing model behavior, with hands-on experience evaluating modern AI systems being a significant advantage.

Requirements

• 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 Haves

• 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).

Responsibilities

• 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.

Benefits

• AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
• 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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed