Senior Machine Learning Engineer, AI Performance
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What you'll do
- 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.
What they're looking for
- 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.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
The role
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.
Key responsibilities
- 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.
About you
In order to set you up for success in this role at Wayve, we’re looking for the following skills and experience:
Essential
- 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.
Desirable
- 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.
#LI-HH1
Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
For more information visit Careers at Wayve.
To learn more about what drives us, visit Values at Wayve
For US candidates only, please visit E-Verify Notice and Participation and Right to Work
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
Company
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