Machine Learning Software engineer

Salary: Up to $250,000

Location: San Francisco preferred (Remote)

Position: Full Time

When a computer vision model has to run in the real world on real hardware, with real power and compute limits the ones that were only ever fine-tuned off someone else's architecture start to fall apart.

You'll own the full lifecycle of the models that power the system from first idea to the moment they're running efficiently on constrained hardware in the field.

You won't be three layers removed from the model, waiting on someone else's architecture decisions.

You'll be the one designing it, training it, evaluating it, and making the calls on how to compress it without giving up the performance that matters with a team that ships fast and takes on the CV problems most companies avoid.

Qualifications and Skills

• Master’s or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field.
• 5+ years of experience in machine learning, deep learning, and computer vision.
• Extensive experience in designing and deploying optimized deep learning models for real-world applications.
• Proficiency in TensorFlow, PyTorch, ONNX, TensorRT, and other ML frameworks.
• Strong experience with model quantization, pruning, knowledge distillation, and hardware acceleration techniques.
• Solid programming skills in Python and C++, with a strong understanding of software optimization.
• Familiarity with embedded platforms such as NVIDIA Jetson, Raspberry Pi, ARM Cortex, Qualcomm AI accelerators, or specialized AI chips.
• Experience with hardware-aware model optimization to maximize inference speed and minimize memory footprint.
• Strong problem-solving skills and ability to work independently on complex technical challenges.

Good to Have

• Feature matching, contrastive learning, or image correlation experience (not required, but may come up in technical interviews, candidates lighter here should be strong across the core areas above).
• Real-time SLAM, visual odometry, or multi-sensor fusion.
• Low-level hardware acceleration (CUDA, OpenCL, or specialized accelerators).
• Familiarity with ROS for robotics integration.

If you are interested please contact Riley @ SR2 Via LinkedIn or Email: Riley.corcoran@sr2rec.com