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Principal Machine Learning Engineer, Data Mining
MotionalBoston, MA · Remote · Lead / Staff / Principal
$240,000 – $330,000
Listed on Motional’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads the machine learning infrastructure that mines autonomous vehicle sensor data to find rare failure modes and edge cases. It's ideal for a principal-level engineer comfortable architecting massive-scale ML systems, mentoring small teams, and bridging technical work across multiple organizations at a driverless-car company moving toward commercial deployment.
Our summary, not Motional’s wording. The full posting is on their site.
Skills this role names
- Agentic systems
- Amazon Web Services (AWS)
- Machine Learning
- Microsoft Azure
- Model Compression & Quantization
- Reinforcement Learning
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What they ask for
Required
- BS in Computer Science, Machine Learning, or related field, or equivalent professional experience
- 12+ years hands-on machine learning engineering experience
- Track record owning end-to-end ML system development from experiment through monitoring
- Exceptional technical maturity with proven impact on company direction
- Ability to solve complex ML infrastructure and production optimization problems others cannot
- Deep experience training large-scale models across distributed GPU clusters
- Demonstrated ownership of technical roadmap balancing competing objectives
- Ability to create clarity from ambiguous problems and define measurable progress
- Generalist ML expertise spanning model training, hardware-optimized deployment, and cloud ML orchestration
- Experience leading cross-team initiatives and mentoring engineers
- Strong written and oral communication skills
Nice to have
- MS or PhD in Computer Science, Machine Learning, or related field
- Tech Lead Manager or direct people management experience with 2–3 engineer teams
- Background in autonomous driving, robotics, or real-time decision-making systems
- Experience in ML data mining, multimodal foundation models, or embodied AI
- Hands-on foundation model pretraining or post-training, data curation, SFT/RLHF, or evaluation design
- Model compression, knowledge distillation, parameter-efficient fine-tuning, or quantization experience
- Large-scale retrieval, search systems, or RL for reasoning experience
- Deep knowledge of enterprise model serving platforms and MLOps
- Experience building agentic pipelines or LLM-driven reasoning workflows
- Portfolio of publications, patents, or significant open-source contributions