Senior Technical Program Manager (Engineering) - AI Tooling & Systems at Deepgram — Remote

Location

USA | Remote

Employment Type

Full time

Location Type

Remote

Department

Engineering

Compensation

Estimated Base Salary $152K – $190K • Offers Equity • Offers Bonus • 10% Annual Bonus

This range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

Please note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits. Company Overview

Company Operating Rhythm

Deepgram is seeking a Senior Technical Program Manager (AI Tooling & Systems) to drive execution of large-scale ML infrastructure and AI tooling initiatives. In this role, you'll own the end-to-end delivery of programs that span model serving infrastructure, ML pipelines, internal AI tooling, and real-time inference systems—working closely with our ML engineers, research teams, and product to unlock capability at scale.

You'll thrive here if you enjoy creating clarity around complex ML system tradeoffs, building tools and processes that accelerate model development and deployment, and partnering across research, engineering, and product to align on technical strategy and execution.

What You'll Do

Own end-to-end delivery of AI infrastructure programs—from model training pipelines and experiment tracking to inference serving and production monitoring

Define technical architecture, integration patterns, and rollout strategies for new ML systems and tooling (e.g., vector databases, model servers, evaluation frameworks, prompt engineering platforms)

Serve as connective tissue between ML research, ML engineering, product, and data teams to align on ML system requirements, capability roadmaps, and deployment timelines

Drive cost and latency optimization for real-time inference workloads at scale

Build lightweight internal tools and processes to accelerate ML iteration cycles (experiment tracking, model versioning, A/B testing infrastructure)

Identify and resolve technical bottlenecks in training pipelines, serving infrastructure, and model evaluation workflows

Work closely with ML practitioners to translate research breakthroughs into scalable, observable systems

You'll Love This Role If You

Are passionate about building ML systems and infrastructure that powers frontier AI applications

Enjoy optimizing inference cost, latency, and throughput for LLM and multimodal workloads at scale

Love solving hard problems at the intersection of ML research and production systems (e.g., distillation, quantization, batching strategies)

Are excited about frontier model serving technologies, vector search, and real-time ML inference

Want to directly enable ML researchers and engineers to iterate faster and ship better models

It's Important That You Have

5+ years of program management or technical leadership in ML infrastructure, ML platforms, or AI tooling (or equivalent)

Strong technical acumen in ML systems—ideally hands-on experience as an ML engineer, systems engineer, or ML infrastructure engineer

Experience coordinating cross-functional ML programs (e.g., model training evaluation serving monitoring)

Proven ability to translate ML/research requirements into robust, scalable infrastructure

Comfortable working in ambiguity and helping teams navigate complex technical tradeoffs (e.g., accuracy vs. latency vs. cost)

Excellent communication with both technical and non-technical stakeholders

Familiarity with high-growth or startup environments

It Would Be Great If You Had

Hands-on experience with model serving frameworks (vLLM, TensorRT, TorchServe, or similar)

Experience optimizing LLM or speech/audio model inference (quantization, distillation, KV-cache optimization, batching strategies)

Familiarity with ML experiment tracking and versioning tools (MLflow, Weights & Biases, DVC, or similar)

Background in feature stores, vector databases, or real-time ML systems

Knowledge of cost optimization for GPU/ML workloads on cloud and on-premise infrastructure

Experience with multi-region model serving or edge deployment

Hands-on with relevant frameworks (PyTorch, CUDA, Hugging Face, etc.) or cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)

Compensation Range: $152K - $190K