Deepgram

2 weeks ago

Applied ML Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Own and streamline the research-to-production pipeline for voice AI models. Partner with research scientists to convert experimental training and evaluation code into robust, reproducible workflows, then build tooling, model release gates, and benchmarking/validation to ship at scale. Optimize training and inference for production latency, throughput, and resource efficiency across hybrid GPU and cloud infrastructure, while instrumenting a feedback loop that accelerates the next model iteration.

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own the research-to-production pipeline by turning research checkpoints into deployed, monitored, scalable production models.
•Partner with research scientists to productionize new models by translating experimental training/evaluation code into robust, reproducible workflows.
•Build and extend tooling and abstractions for moving models through training, evaluation, packaging, and deployment with reproducibility.
•Design and own automated model release gates using evaluation, regression detection, and quality/latency/throughput checks.
•Optimize model serving and operational readiness across hybrid infrastructure, including instrumentation for a feedback loop back to research.

Pay and Benefits

Salary: USD 150,000 - 220,000 annually

Equity and Bonus:Equity

Key Requirements

•Strong software engineering fundamentals with proficiency in Python and experience writing production-quality, well-tested ML code.
•Hands-on experience taking ML models from research/prototype stage into production at scale, including shipping and operating them.
•Working understanding of the modern deep learning stack (e.g., PyTorch) and the realities of training, evaluating, and serving large models.
•Experience building ML pipelines and tooling (training orchestration, evaluation harnesses, model packaging, deployment, or CI/CD for models).
•Familiarity with serving and inference optimization (latency, throughput, batching, and resource efficiency) and operating across distributed systems and GPU compute.

Experience:Voice AISpeechAudioReal-time/streaming MLDeep learning

Skills:Builder mindsetCollaborationAdaptabilityContinuous learning

Tech Stack:PythonPyTorchCI/CDTraining orchestrationEvaluation harnessesModel packagingDeploymentDistributed systemsGPU computeQuantizationDistillationCompilationRuntime tuning

Company Brief

Deepgram

Deepgram builds a real-time Voice AI platform delivering speech-to-text, text-to-speech, and voice-agent APIs for developers and enterprises, focusing on low-latency, high-accuracy voice models and scalable deployment options.

Industry: API Platforms

Company Size: Medium (51 to 250 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2015

Glassdoor: 4.5

Website LinkedIn Glassdoor

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Deepgram

2 weeks ago

Applied ML Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own the research-to-production pipeline by turning research checkpoints into deployed, monitored, scalable production models.
•Partner with research scientists to productionize new models by translating experimental training/evaluation code into robust, reproducible workflows.
•Build and extend tooling and abstractions for moving models through training, evaluation, packaging, and deployment with reproducibility.
•Design and own automated model release gates using evaluation, regression detection, and quality/latency/throughput checks.
•Optimize model serving and operational readiness across hybrid infrastructure, including instrumentation for a feedback loop back to research.

Pay and Benefits

Salary: USD 150,000 - 220,000 annually

Equity and Bonus:Equity

Key Requirements

•Strong software engineering fundamentals with proficiency in Python and experience writing production-quality, well-tested ML code.
•Hands-on experience taking ML models from research/prototype stage into production at scale, including shipping and operating them.
•Working understanding of the modern deep learning stack (e.g., PyTorch) and the realities of training, evaluating, and serving large models.
•Experience building ML pipelines and tooling (training orchestration, evaluation harnesses, model packaging, deployment, or CI/CD for models).
•Familiarity with serving and inference optimization (latency, throughput, batching, and resource efficiency) and operating across distributed systems and GPU compute.

Experience:Voice AISpeechAudioReal-time/streaming MLDeep learning

Skills:Builder mindsetCollaborationAdaptabilityContinuous learning

Tech Stack:PythonPyTorchCI/CDTraining orchestrationEvaluation harnessesModel packagingDeploymentDistributed systemsGPU computeQuantizationDistillationCompilationRuntime tuning

Company Brief

Deepgram

Industry: API Platforms

Company Size: Medium (51 to 250 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2015

Glassdoor: 4.5

Website LinkedIn Glassdoor