ML Engineer — Real-time Speech

Sellsig; Minnesota; Vor Ort; USD 100.000 - 130.000; Vollzeit

Zusammenfassung

Sellsig is seeking an experienced ML Engineer to build real-time speech and language processing pipelines. The role focuses on developing streaming ASR and objection detection systems.

With at least 4 years of experience in applied ML, candidates should excel in Python and have practical exposure to low-latency implementations. The position emphasizes driving end-to-end latency and integrating modern LLM tools.

Qualifikationen

• 4+ years in ML/applied ML, with production work in speech, NLP, or real-time inference.
• Experience deploying models in low-latency, streaming settings.
• Comfortable owning ambiguous, research-flavored problems end to end.

Aufgaben

• Build and tune streaming ASR and intent/objection-detection pipelines.
• Drive down end-to-end latency while holding accuracy.
• Evaluate and integrate speech and LLM providers.

Kenntnisse

Machine Learning

Python

ASR/diarization

Low-latency streaming

Evaluation metrics

WebRTC

Tools

Deepgram

AssemblyAI

Jobbeschreibung

Build the real-time speech and language pipeline behind the coach — streaming transcription, speaker separation, and the models that detect objections and intent fast enough to fire a cue before the rep flinches. You care about sub-second latency as much as accuracy.

What you'll do

• Build and tune streaming ASR, diarization, and intent/objection-detection pipelines for live calls.
• Drive down end-to-end latency while holding accuracy; own the speed/quality trade-offs.
• Evaluate and integrate speech and LLM providers; build evals and guardrails for production quality.
• Instrument the pipeline so quality and latency regressions are caught before reps feel them.
• Partner with product to turn detected signals into cue cards that actually help mid-call.

What we're looking for

• 4+ years in ML/applied ML, with production work in speech, NLP, or real-time inference.
• Strong Python and experience deploying models in low-latency, streaming settings.
• Practical grasp of ASR/diarization and modern LLM tooling and prompting.
• Rigorous about evaluation: you measure before you ship and after.
• Comfortable owning ambiguous, research-flavored problems end to end.
• Experience with streaming ASR providers (Deepgram, AssemblyAI) or self-hosted speech models.
• WebRTC/audio engineering or DSP background.
• Building LLM evals, judges, or RAG systems in production.

How we hire

Our process has a few stages — it's a real role, so we take it seriously on both sides:

• Intro call with the hiring manager — the role, your background, and what you want next.
• Team conversations — meet the people you'd work with day to day.
• A practical exercise drawn from real work — no whiteboard puzzles, no weekend-eating take-homes.
• An on-site visit to our Saint Paul office to meet the team in person.
• Final conversation and references, then an offer.

We move as quickly as we can and keep you posted at every step.

Not a perfect match?

If you'd be great at this work, tell us how you'd approach it. We read every note.