Senior Machine Learning Engineer

Who you are

• Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost)
• Strong hands-on experience training and iterating on deep learning models in PyTorch (not just using high-level tooling)
• Strong proficiency with at least one relevant stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL) and confidence learning adjacent frameworks quickly
• Comfort operating at multiple levels of abstraction — from high-level model behaviour down to low-level kernel/runtime execution
• Familiarity with model optimisation concepts such as quantisation and/or distillation (hands-on is a strong signal, but not a strict requirement if the fundamentals are solid)
• Ability to reason across multiple levels of abstraction—from high-level model behaviour down to practical runtime/latency implications
• Strong engineering fundamentals and collaboration skills
• Experience working on models that must meet tight latency / efficiency constraints (edge, embedded, real-time, or similarly constrained production settings)
• Exposure to ML systems spanning training → evaluation → deployment handoff (even if you’re not writing kernels day-to-day)
• Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and handling system-level constraints

What the job involves

• We’re looking for a Senior Machine Learning Engineer to join a high-ownership team responsible for delivering production-ready model releases as our OEM engagements and release cadence accelerate. This is an applied, delivery-focused MLE role—ideal for engineers who love shipping real systems and iterating quickly
• You’ll work on taking models from “works in training” to “meets product constraints,” partnering closely with teams downstream (e.g., inference/performance specialists) to ensure models are ready for deployment on-vehicle
• As model capability grows, you’ll help keep the system within tight runtime constraints using a practical model optimisation techniques (e.g., quantisation, distillation, low-rank methods) where appropriate
• Own end-to-end delivery of model releases, from initial requirements through training, evaluation, iteration, and final readiness for deployment
• Train and iterate on PyTorch models with a strong experimental approach (hypothesis-driven iteration, ablations, clear evaluation criteria)
• Debug and improve model performance using strong analytical skills—identifying regressions, root-causing issues, and proposing fixes
• Apply optimisation techniques (e.g., quantisation and distillation where beneficial), understanding trade-offs and when methods are appropriate
• Collaborate cross-functionally with adjacent ML and performance engineering teams to hand off models, define bottlenecks, and align on optimisation priorities
• Communicate clearly with stakeholders to align on delivery timelines, trade-offs, and readiness criteria

Benefits

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.