Machine Learning Engineer

Guava; Los Angeles (CA); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Health, dental, and vision coverage

401(k) match

Flexible PTO

Equity

Job summary

Guava is seeking engineers to build the best tools in voice, working across the model lifecycle from experimentation to production deployment for real, regulated phone calls.

You will design, train, and evaluate ML models, build LLM-based components, and ensure scalable, low-latency inference with cost awareness. You’ll collaborate with governance teams and stay ahead of research to push practical, production-ready ideas.

Qualifications

• CS fundamentals, data structures, and algorithms.
• Strong foundation in linear algebra and probability.
• Production ML experience in NLP/speech is preferred.

Responsibilities

• Design, train, and evaluate ML models across Guava's platform: ASR, TTS, intent recognition, dialogue systems, summarization, and related NLP/NLU tasks.
• Build and maintain LLM-based components in production—prompting, fine-tuning, structured outputs, tool calling, and retrieval where appropriate.
• Pipeline and manage large, real-world datasets, including labeling workflows and data quality checks.
• Take models from prototype to production: benchmark performance, validate against target accuracy, and ship with monitoring in place.
• Partner with the Call Review and LLM working groups to root-cause production issues — ASR errors, hallucinations, latency, turn-taking — and turn them into model improvements.
• Contribute to model governance: maintain the model inventory, document validation results, and support risk assessment for new deployments.
• Work closely with platform and infrastructure engineers to optimize inference latency and cost at scale.
• Stay current with ML/NLP research and bring practical, production-ready ideas back to the team.

Skills

CS fundamentals

Python

NLP/ML experience

LLM deployment

Data handling

Independent thinker

GPU training

Education

MS or PhD in CS/ML

Tools

GPU acceleration

Production ML tooling

Job description

Location: In office, Downtown Los Angeles (DTLA)

Type: Full-time

Compensation: $140,000 – $180,000 + equity

About Guava

Guava is the voice platform built for regulated industries — the calls that have to be right. Trained on 10B+ live agent minutes and in regulated production since 2013, Guava gives enterprises a single, compliant system for every second of a call. We serve healthcare systems, banks, insurance carriers, BPOs, and tech-native companies that need voice infrastructure they can stake their business on.

We are a fast-growing AI company with deep engineering roots in Stanford, MIT and JPL.

The Role

Guava is looking for Engineers to build the best tools in voice. You will work across the model lifecycle: designing experiments, training and evaluating models, and shipping them into a production system that handles real, regulated phone calls at scale.

This is a hands-on engineering role. You will be close to the data, close to production, and close to the outcomes your models directly affect call quality, accuracy, and the trust our customers place in the platform.

What You'll Do

• Design, train, and evaluate ML models across Guava's platform: ASR, TTS, intent recognition, dialogue systems, summarization, and related NLP/NLU tasks.
• Build and maintain LLM-based components in production — prompting, fine-tuning, structured outputs, tool calling, and retrieval where appropriate.
• Pipeline and manage large, real-world datasets, including labeling workflows and data quality checks.
• Take models from prototype to production: benchmark performance, validate against target accuracy, and ship with monitoring in place.
• Partner with the Call Review and LLM working groups to root-cause production issues — ASR errors, hallucinations, latency, turn-taking — and turn them into model improvements.
• Contribute to model governance: maintain the model inventory, document validation results, and support risk assessment for new deployments.
• Work closely with platform and infrastructure engineers to optimize inference latency and cost at scale.
• Stay current with ML/NLP research and bring practical, production-ready ideas back to the team.

What We're Looking For

• Strong computer science fundamentals: algorithms, data structures, and systems programming.
• Solid math and statistics foundation — linear algebra, probability, and their application to machine learning.
• Production experience building, training, or deploying ML models, ideally in NLP, speech, or signal processing.
• Fluent in Python, with mature software engineering practice: clean code, testing, and a disciplined debugging loop.
• Comfortable working with large datasets and modern numerical methods, including GPU-accelerated training.
• Built something real with an LLM — structured outputs, tool calling, fine-tuning, or an LLM embedded in a production workflow.
• Independent thinker who is comfortable owning a problem end to end, from data to deployed model.
• Based in or willing to work from Guava's Downtown Los Angeles office.

Nice to Haves

• Experience with speech technologies specifically — ASR, TTS, speaker/voice biometrics, or telephony-adjacent audio processing.
• Background in NLP: intent recognition, dialogue systems, summarization, or semantic search.
• Experience with model governance, evaluation frameworks, or production ML monitoring.
• Advanced degree (MS or PhD) in computer science, machine learning, or a related quantitative field.
• Experience at an early-stage or fast-moving startup.

Why Guava

• Work on models that run in live, regulated production — not a research sandbox.
• Own real problems end to end, with direct access to senior engineering leadership.
• Join a technical team with deep roots in speech, NLP, and applied ML.
• Competitive base salary and early-stage equity.
• Full health, dental, and vision coverage; employer 401(k) match; flexible PTO.

Guava is an equal opportunity employer. We welcome applicants of all backgrounds and are committed to building a diverse and inclusive team.