OPT AI Research Engineer Jobs
AI Research Engineer roles sit at the intersection of machine learning research and production systems, making them one of the most competitive OPT job categories. Most positions require a master's or PhD and fall squarely within STEM OPT, giving you up to 36 months of work authorization to establish yourself in U.S. research teams.
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Get Access To All JobsWhy RoboForce
RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a Senior / Staff AI Research Engineer, Real-Time Inference to make embodied AI practical on the edge. In this role, you will drive the full stack of model optimization — from CUDA kernel engineering to quantization and compression — to deploy high-performance AI models on edge compute platforms powering RoboForce robots in the field.
Responsibilities
- Develop and optimize inference pipelines for embodied AI models (VLA, perception, world models) targeting real-time execution on edge hardware such as NVIDIA Jetson platforms.
- Implement CUDA-level optimizations including custom kernels, memory layout tuning, and hardware-aware graph compilation to minimize model latency.
- Apply and advance model compression techniques — quantization (INT8/FP16/INT4), pruning, distillation, and structured sparsity — to achieve production-grade throughput on constrained devices.
- Profile and debug end-to-end inference stacks using tools such as NSight, TensorRT, and Triton to identify and eliminate performance bottlenecks.
- Collaborate with ML research and robotics teams to co-design model architectures that meet real-time control-loop latency requirements.
- Establish benchmarking frameworks to evaluate model performance across latency, throughput, power consumption, and accuracy tradeoffs on target hardware.
Requirements
- Master's degree in Computer Science, Electrical Engineering, or related field with 4+ years of experience, or a PhD degree.
- Deep expertise in CUDA programming, GPU architecture, and low-level kernel optimization, including custom kernel authoring with tools such as Triton.
- Hands-on experience with model quantization, pruning, distillation, and deployment using frameworks such as TensorRT, ONNX Runtime, TVM, or Triton.
- Proficiency in C++ and Python; strong systems programming and performance profiling skills.
- Experience deploying ML models on edge or embedded hardware (e.g., NVIDIA Jetson, Orin, or equivalent ARM/GPU SoCs).
- Requires 5 days/week in-office collaboration with the teams.
Bonus Qualifications
- Familiarity with embodied AI models — VLA, multimodal transformers, or diffusion-based policies — and their inference characteristics.
- Familiarity with compiler-based optimization pipelines such as XLA, torch.compile, or MLIR for graph-level model acceleration.
- Understanding of robotics system constraints such as control-loop timing, sensor fusion latency, and memory bandwidth limits on edge SoCs.
- Publication or production work in efficient deep learning or on-device ML systems.
Benefits
- Competitive stock options/equity programs.
- Health, dental, and vision insurance, 401(k) plan.
- Visa sponsorship and green card support for qualified candidates.
- Lunches and dinners, a fully stocked kitchen, and regular team-building events.
Why RoboForce
RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a Senior / Staff AI Research Engineer, Real-Time Inference to make embodied AI practical on the edge. In this role, you will drive the full stack of model optimization — from CUDA kernel engineering to quantization and compression — to deploy high-performance AI models on edge compute platforms powering RoboForce robots in the field.
Responsibilities
- Develop and optimize inference pipelines for embodied AI models (VLA, perception, world models) targeting real-time execution on edge hardware such as NVIDIA Jetson platforms.
- Implement CUDA-level optimizations including custom kernels, memory layout tuning, and hardware-aware graph compilation to minimize model latency.
- Apply and advance model compression techniques — quantization (INT8/FP16/INT4), pruning, distillation, and structured sparsity — to achieve production-grade throughput on constrained devices.
- Profile and debug end-to-end inference stacks using tools such as NSight, TensorRT, and Triton to identify and eliminate performance bottlenecks.
- Collaborate with ML research and robotics teams to co-design model architectures that meet real-time control-loop latency requirements.
- Establish benchmarking frameworks to evaluate model performance across latency, throughput, power consumption, and accuracy tradeoffs on target hardware.
Requirements
- Master's degree in Computer Science, Electrical Engineering, or related field with 4+ years of experience, or a PhD degree.
- Deep expertise in CUDA programming, GPU architecture, and low-level kernel optimization, including custom kernel authoring with tools such as Triton.
- Hands-on experience with model quantization, pruning, distillation, and deployment using frameworks such as TensorRT, ONNX Runtime, TVM, or Triton.
- Proficiency in C++ and Python; strong systems programming and performance profiling skills.
- Experience deploying ML models on edge or embedded hardware (e.g., NVIDIA Jetson, Orin, or equivalent ARM/GPU SoCs).
- Requires 5 days/week in-office collaboration with the teams.
Bonus Qualifications
- Familiarity with embodied AI models — VLA, multimodal transformers, or diffusion-based policies — and their inference characteristics.
- Familiarity with compiler-based optimization pipelines such as XLA, torch.compile, or MLIR for graph-level model acceleration.
- Understanding of robotics system constraints such as control-loop timing, sensor fusion latency, and memory bandwidth limits on edge SoCs.
- Publication or production work in efficient deep learning or on-device ML systems.
Benefits
- Competitive stock options/equity programs.
- Health, dental, and vision insurance, 401(k) plan.
- Visa sponsorship and green card support for qualified candidates.
- Lunches and dinners, a fully stocked kitchen, and regular team-building events.
See all 399+ OPT AI Research Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as an AI Research Engineer
Lead with publications and research output
AI Research Engineer hiring managers review your publication record before your resume. List arXiv preprints, conference papers, and workshop contributions prominently. Even one accepted NeurIPS or ICML paper substantially separates you from candidates without published work.
Target STEM-designated roles explicitly
Confirm the role is classified under a STEM CIP code before applying. AI Research Engineer positions typically fall under Computer Science or Electrical Engineering codes, which qualify for the 24-month STEM OPT extension beyond your initial 12 months.
Highlight your E-Verify employer requirement early
STEM OPT extension requires your employer to be E-Verify enrolled. Raise this in early conversations to avoid investing weeks in an interview process with an employer who cannot legally support your extension.
Showcase production-ready research skills
Many labs distinguish researchers who only prototype from those who can deploy. Demonstrate PyTorch or JAX proficiency alongside systems experience. Candidates who bridge research and engineering compress the sponsorship risk employers perceive during OPT periods.
Build a public portfolio of reproducible work
Open-source implementations of your research, well-documented GitHub repositories, and Hugging Face model cards all serve as persistent proof of skill. Hiring teams at research-focused employers frequently review these before extending interview invitations to OPT candidates.
Apply before your OPT start date when possible
Research roles have long hiring cycles, often eight to twelve weeks from first contact to offer. Starting your search three to four months before authorization begins gives employers the runway they need to complete approvals before your start date.
AI Research Engineer OPT: Frequently Asked Questions
Do AI Research Engineer roles qualify for the STEM OPT extension?
Yes, in most cases. AI Research Engineer positions are typically classified under Computer Science or Electrical Engineering CIP codes, both of which qualify for the 24-month STEM OPT extension. You should confirm the specific CIP code on your I-20 matches a STEM-designated field and verify that the employer is E-Verify enrolled before accepting an offer.
How do employers typically view OPT candidates for research engineering roles?
Research-focused employers, including university labs, AI startups, and large technology companies, are generally more comfortable hiring OPT candidates than employers in other industries because they already manage visa sponsorship for international researchers regularly. Your research output, technical depth, and alignment with the team's focus area matter more than your visa status in most screening conversations.
Where can I find AI Research Engineer jobs that are open to OPT candidates?
Migrate Mate filters job listings specifically for visa-sponsored and OPT-friendly roles, so you're not sifting through postings from employers who won't support work authorization. Browsing AI Research Engineer listings on Migrate Mate surfaces positions where sponsorship is already confirmed, which saves significant time during a job search with tight authorization deadlines.
Can I work as an AI Research Engineer on post-completion OPT before my STEM extension is approved?
Yes. You can begin working as soon as your OPT EAD card is valid and your employment is in a role directly related to your degree. You apply for the STEM extension up to 90 days before your initial OPT expires, and USCIS provides a 180-day automatic extension while the application is pending, so your authorization remains continuous.
What does the mandatory training plan requirement mean for AI Research Engineer roles?
During the STEM OPT extension, your employer must complete Form I-983, which outlines how the role provides practical training connected to your degree. For AI Research Engineer positions this is usually straightforward since the work directly involves applying advanced machine learning and computer science skills. Your employer's designated representative signs the plan and is responsible for reporting any material changes to your DSO.