Mid Level Integration Engineer Jobs
Mid level integration engineer jobs go to engineers ready to own end-to-end integration projects, guide junior teammates, and make architectural decisions without constant oversight. Hiring covers 15% remote and hybrid settings across Electronics & Hardware, Technology & Software, and Automotive, with North Carolina State University, DoorDash, and Micron Technology competing for engineers at this level now.
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Showing 5 of 58+ Mid Level Integration Engineer jobs
INTRODUCTION
We are looking for the best
About us
42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles. We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.
About The Role
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
Responsibilities
- 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
Qualifications
- 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 Qualifications
- 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
INTRODUCTION
We are looking for the best
About us
42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles. We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.
About The Role
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
Responsibilities
- 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
Qualifications
- 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 Qualifications
- 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
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Find JobsMid Level Integration Engineer Job Market
Who's Hiring
Top Industries Hiring
- Electronics & Hardware15
- Technology & Software12
- Automotive6
- Education5
- Aerospace & Defense4
Mid Level Integration Engineer Jobs: Frequently Asked Questions
How do I get a mid level integration engineer job?
Position your experience around ownership, not just contribution. Highlight integrations you designed or delivered end to end, APIs you built or consumed, and any time you resolved a production issue or led a technical decision. Tailor your resume to show depth in at least one integration platform or protocol, and be ready to walk through architecture choices in interviews.
Which companies hire mid level integration engineers?
Companies hiring mid level integration engineers right now include North Carolina State University, DoorDash, and Micron Technology, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a broad mix of enterprise software vendors, healthcare systems, financial services firms, and logistics operators that run complex data and application integration pipelines.
Are there remote mid level integration engineer jobs?
Yes, remote and hybrid options are common at this level. About 15% of mid level integration engineer openings are remote or hybrid as of November 2026, reflecting how much integration work happens across distributed systems and teams. On-site roles still exist, typically at companies with strict data security or compliance requirements.
How do I move up to a mid level integration engineer role?
The jump from entry level to mid level comes from accumulating project ownership over time. Building depth in integration patterns like ETL pipelines, REST and event-driven APIs, or iPaaS platforms matters more than breadth. Employers look for engineers who have delivered integrations that reached production, adapted when requirements shifted, and developed enough judgment to work with minimal supervision.
Which industries hire the most mid level integration engineers?
Mid Level integration engineer roles concentrate in Electronics & Hardware, Technology & Software, and Automotive, based on current listings on Migrate Mate as of November 2026. These sectors drive demand because they depend on connecting disparate systems, whether linking EHR platforms in healthcare, payment rails in finance, or order management systems in retail and logistics.