Applied Intuition
AI, Physical AI, Automotive, Defense, Trucking, Construction, Mining, Agriculture
EngineeringManagerML,SelfDrivingSystems
“Engineering Manager - ML, Self-Driving Systems at Applied Intuition. Skills: ML, Deep Learning, ML development and deployment, ML training pipelines at scale, ML model deployment to embedded/edge hardware, Python, C++, Architecture transitions. Set the technical direction across multiple ML workstreams: the foundation model, shared backbone, and task heads that enable end-to-end driving, plus agent prediction and model optimization. Lead rapid training and iteration cycles across your teams”
What You'll Achieve.
Models ship into production vehicles on timelines measured in months; Models ship into production vehicles on quarterly release cycles with direct impact on customer programs; Models must meet rigorous V&V standards for vehicles on public roads
Industry & Context.
The core challenge is commonization across verticals so one model serves ADAS, trucking, and mining without per-vertical forks; Accountable for models running on customer hardware, not benchmarks on a leaderboard
Primarily work from their Applied Intuition office 5 days a week, Occasional remote work may be possible
What They're Looking For.
Must Have
5+ years in deep learning, Hands-on experience guiding teams in state-of-the-art ML development and deployment, 4+ years managing deeply technical product development teams, Experience building ML training pipelines at scale: data management, distributed training, experiment tracking, model evaluation, Track record deploying ML models to embedded or edge hardware, including quantization, pruning, and device-specific optimizations, Software engineering in Python and C++, comfortable across the full stack from training code to onboard inference, Experience managing through architecture transitions (classical to learned, modular to end-to-end) while maintaining production reliability
Nice to Have
Familiarity with occupancy-based scene representations, dense prediction heads, or sparse query-based architectures, Experience with closed-loop simulation for ML model evaluation (neural sim, log sim, scenario-based testing), Background in data flywheel design: automated ingestion, curation, quality monitoring, and dataset refresh workflows, Multi-domain ML development: training one model architecture across different sensor configs, vehicle types, or geographies, Experience at an AV company that has shipped autonomy to production
What You'll Do.
Set the technical direction across multiple ML workstreams: the foundation model
and task heads that enable end-to-end driving
plus agent prediction and model optimization
Lead rapid training and iteration cycles across your teams
Work directly with OEM customers and program teams to translate vehicle platform constraints into model architecture and delivery plans
Own the offboard ML pipelines that determine iteration speed: training infrastructure
and the evaluation systems that connect offboard metrics to on-vehicle driving outcomes
Manage the full model lifecycle from prototype to embedded deployment
including training at scale
and device-specific optimizations
and retain engineers in a competitive market
Shape the team's structure
and technical standards as it continues to grow
How You'll Work.
Team & Collaboration
Work directly with OEM customers and program teams
Process & Methodology
Lead rapid training and iteration cycles, Models ship into production vehicles on quarterly release cycles, Manage the full model lifecycle from prototype to embedded deployment
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