Software Integration Engineer, Autonomy

Sunnyvale, United StatescontractOn Site

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mid

$150k–$205k

H1B: no

42dot Software Integration Engineer, Autonomy at 42dot integrates ML models onto on-board vehicle systems and optimizes inference for embedded automotive hardware. This role builds the model deployment pipeline and defines interface contracts between ML models and on-board software.

Responsibilities

Great models are only as good as the system they run on. This role sits at the boundary between the ML autonomy team's models and the on-board system, taking what the research and ML teams build and turning it into something that runs reliably, in real time, on real vehicle hardware. 42dot is looking for builders who want to own the last and hardest mile between a model that works in an offline eval and a model that works in a vehicle.
Founding Team Opportunity
The on-board integration architecture is being defined right now, with no legacy deployment stack to retrofit. An early hire here will help shape:
The model deployment pipeline, from training-time artifact to vehicle-ready binary
Inference optimization strategy and tooling for the whole autonomy stack
The interface contract standard between ML models and on-board software
Integrate ML models (end-to-end driving, VLA, world models) onto the on-board compute and runtime stack
Optimize model inference for latency, throughput, memory, and power on embedded automotive hardware
Build and maintain the model deployment pipeline, including conversion, validation, and rollout
Stabilize on-vehicle ML execution by diagnosing runtime failures, timing violations, and resource contention
Define interface contracts between ML models and the rest of the on-board software stack
Work closely with the ML autonomy team to close the loop between deployment constraints and model design

Qualification

A track record of grit and ownershipBachelor'sStrong C++ and Python skillsInterview Process

Required

A track record of grit and ownership, with success moving difficult problems from ambiguity to successful deployment
Bachelor's, Master's, or PhD in CS, EE, Robotics, or related field
5+ years of experience in systems or software engineering, with meaningful time in ML inference, embedded systems, or real-time software
Experience deploying and optimizing ML models in production, including on resource-constrained hardware
Strong C++ and Python skills, with experience working across the ML and systems boundary

Preferred

Background in autonomous driving or robotics on-board software
Experience with inference optimization frameworks such as TensorRT, ONNX Runtime, and automotive SoCs
Experience with real-time operating systems and safety-critical software practices
Experience building CI/CD and automated validation pipelines
Interview Process
Application Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring
The screening procedures may vary depending on the position, schedule, or other circumstances.

Company

42dot

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Source: ashby

Active within the past hour

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C++PythonML inferenceembedded systemsreal-time softwareTensorRT

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