Perception Engineer
Perception Engineer owning outcomes for autonomous mining vehicles. Responsible for sensor selection, model adaptation to new sites/domains, diagnosing failures, data strategies, and translating customer needs into technical KPIs and solutions. Requires strong systems understanding of perception/full stack and real-world deployment experience.
About the job
Responsibilities
- Design and implement perception systems to enable new product capabilities, leveraging existing model heads and solving new perception tasks.
- Design the data strategy for a new deployment: what to collect, how much, and how to label it, to get a model production-ready.
- Make build-vs-reuse tradeoffs when standing up new product lines, including output types, KPIs, and collaboration points with the core perception team.
- Triage model and system issues in the field — timestamps, data quality, calibration, and similar — and decide whether to resolve them directly or work with the core platform team to fix them upstream.
- Own perception outcomes for autonomous mining vehicles operating across active customer sites.
- Select sensor sets for new vehicles, adapt existing models to new domains and operational design domains.
- Diagnose why a model underperforms on a new site and decide when to fix it yourself versus routing it back to the core team.
- Work at the intersection of perception, planning, controls, and customer requirements, translating mining customer needs into technical approaches and KPIs.
- Collaborate daily with the core autonomy platform team, customer engineering, and BD.
- Help stand up new product lines alongside developing existing ones.
Requirements
- Strong systems-level understanding of perception (and ideally planning/controls) — enough to reason about the full stack, even without owning model training personally.
- A product-owner mindset: motivated by customer success and problem ownership more than by being on the cutting edge of modeling research.
- Experience diagnosing real-world model failures in deployed systems and driving them to resolution, whether hands-on or through cross-team negotiation.
- Comfort working with ambiguous, evolving requirements from customers and translating them into technical specs.
- Familiarity with PyTorch, ONNX, TensorRT, Docker, Python, some C++.
Nice-to-Haves
- Experience with internal ML pipeline/orchestration tooling (e.g., Flyte).
- Experience with offroad autonomy.
Compensation
- Base salary range: $180,000 - $255,000 USD annually.
- Total compensation package may also include equity, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off.
Skills
PyTorch, Onnx, TensorRT, Docker, Python, C++, Perception Systems, Ml Pipelines, Flyte
Similar jobs
ML Engineering jobs
Mirage
New York, NY
Build and deploy agentic systems that power AI-driven creative video workflows. The role requires 5+ years of experience, production ML or agentic pipeline development, context engineering, and expertise in evaluation and agent infrastructure.
Mirage
New York, NY
Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.
Taste Labs
San Francisco, CA
Build evaluation methods, RL environments, agent tooling, and scalable infrastructure that make subjective qualities such as design and taste measurable for frontier AI models. The role requires experience with evaluations, RL environments, ML or post-training, plus strong backend engineering skills.
Mirage
New York, NY
Build and scale generative video and multimodal models, optimizing training and inference for efficiency, throughput, and ultra-low latency. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and the ability to move research models into production.
Earnin
Mountain View, CA
Build production-grade AI agents, evaluation infrastructure, and developer tooling that make AI-assisted engineering faster, safer, and reusable across teams. The role requires software engineering experience, platform or internal developer-product experience, and hands-on expertise with LLM integration and orchestration.