Senior Machine Learning Engineer II

Axon•Seattle, WA

40d•$156,750 - $250,800•Hybrid

About The Position

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact We are seeking a seasoned Machine Learning Engineer to join a new team building agentic video and multimodal reasoning systems. As a senior engineer on this team you will own the systems and infrastructure that turn cutting-edge research into safe, reliable, and scalable AI capabilities — supporting our company’s mission to accelerate justice, protect truth, and save lives. Working shoulder to shoulder with research scientists, you will build everything that stands between a promising model and a production system operating on video and multimodal data at scale: the data and ingest pipelines, the training and evaluation infrastructure, the inference and serving stack, the retrieval and orchestration layers behind agentic reasoning, and the reliability, security, and privacy guarantees that Axon’s customers depend on. Scientists define the models and the metrics; you build the platform that lets them move fast and lets those models ship. At Axon, we Aim Far. We think big with a long-term view because we want to reinvent the world to be a safer, better place. If you’re excited about this role and our mission to Protect Life but your experience doesn’t align perfectly with every qualification listed here, we encourage you to apply anyway. You may be just the right candidate for this or other roles.

Requirements

Bachelor’s Degree in Computer Science, Engineering, Physics, Mathematics or an equivalent highly technical field.
10+ years of software engineering experience and a proven track record of architecting, operating, and maintaining large-scale distributed platforms in production.
Deep experience building systems across the ML lifecycle: data pipelines, training and evaluation infrastructure, model serving and inference at scale, and production monitoring.
Experience with large-scale data systems handling video or other high-volume, high-dimensional multimodal data.
Strong software engineering fundamentals and proficiency in Python, along with familiarity with ML frameworks such as PyTorch or TensorFlow.
Hands-on experience operating cloud infrastructure at scale (AWS, GCP, or Azure), including distributed systems and container orchestration.
Excellent problem-solving skills and the ability to dive into system architecture, design, performance metrics, code, test plans, project plans, deployments, and operations.
Strong communication skills and comfort partnering closely with scientists, engineers, and product managers.

Nice To Haves

Master’s or PhD in Computer Science, Engineering, or an equivalent highly technical field.
Experience building video processing or video understanding pipelines at scale.
Experience with retrieval systems, vector search, and RAG infrastructure.
Experience serving foundation models, or building agentic systems and LLM tool-use orchestration.
Familiarity with responsible AI, de-identification, and privacy-preserving techniques.

Responsibilities

Own end to end the systems and infrastructure that take multimodal and video models from research prototype to reliable, scalable production.
Design and operate data and ingest pipelines for large-scale video and multimodal corpora — storage, processing, labeling, and retrieval.
Build and scale training and evaluation infrastructure, and turn scientists’ evaluation methodology into automated, reproducible measurement systems.
Build inference and serving systems for real-time and batch multimodal workloads, optimizing latency, throughput, and cost.
Build the retrieval, indexing, and embedding infrastructure (vector search at scale) and the orchestration and tool-use plumbing behind agentic reasoning over video and multimodal data.
Ensure the platform is secure, privacy-preserving, and responsible by design.
Set technical direction for the team’s systems, raise the engineering bar, and mentor other engineers.