Research Engineer Agent Intelligence & Evaluation

Voice agents fail in ways traditional software doesnt. ASR confidence drops on an accent and a tool call misfires. Latency breaks turn-taking and the LLM hallucinates a policy. A model swap silently regresses production and nobody catches it for a week. 

Were building self-healing voice agents for enterprise customer support. This role owns the intelligence layer: the evals that catch failures before shipping the observability that traces them across the pipeline and the feedback loops that let agents fix themselves 

 What youll own 

Evaluation infrastructure. Audio-native metrics for barge-in prosody and turn-taking. Adversarial datasets across accents and edge cases. LLM-as-judge rubrics for task success tool-use correctness and recovery. 

Observability across the pipeline. Tracing that correlates audio STT LLM reasoning tool calls and TTS to a single conversation. Analysis and alerting that surfaces cascade failures instead of hiding them. 

Self-improvement systems. Mine production traces for failure patterns generate targeted training or prompt data validate fixes with adversarial replay and guardrail against regressions.

Qualifications :

Who were looking for 
3 to 5 years in ML engineering research engineering or applied research. Strong Python and modern ML tooling. Depth in at least two of: speech and audio models LLM agent systems and eval or observability infrastructure. 

Youve shipped something non-trivial where research met production. You read papers spot when a benchmark measures the wrong thing and translate ideas from Interspeech ACL or NeurIPS into systems that run on real traffic. Publications welcome not required. 
 

Nice to have 
Real-time systems or telephony experience. Work on RLHF DPO or synthetic data pipelines. Familiarity with enterprise deployment (SOC 2 PII data residency). 
 

What youll get 
Senior seat on a small team where research and production arent separate orgs. Real enterprise conversation data under proper governance. Meaningful equity autonomy over tooling and support to publish. 

Additional Information :

Candidates are responsible for safeguarding sensitive company data against unauthorized access use or disclosure and for reporting any suspected security incidents in line with the organizations ISMS (Information Security Management System) policies and procedures.

Remote Work :

No

Employment Type :

Full-time