Senior Software Engineer - Model Evaluation & AI Systems

What You'll Do
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
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
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).