Wayve
United States
Research Scientist - Multi-Embodiment Generalist Agent (MEGA)
- AI
- Machine Learning
- Pytorch
AI Summary
Lead the design, implementation, and scaling of the company's ML infrastructure and production AI systems. Architect and operate end-to-end ML Ops pipelines across training, evaluation, deployment, observability, and optimization. Bring Staff/Principal-level experience in production ML systems, distributed systems, cloud infrastructure, and ML frameworks.
Key Highlights
Staff/Principal-level ML Ops role for a fast-growing AI startup
Lead end-to-end ML infrastructure and production AI systems
Work across training, evaluation, deployment, observability, and optimization
Relocation package available or remote option for out-of-state applicants
Benefits include equity options, sign-on bonus, health coverage, and 401k
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Key Responsibilities
Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
Collaborate with researchers to productionize models and accelerate training/inference pipelines.
Establish ML Ops best practices, internal standards, and cross-team tooling.
Mentor engineers and influence architectural direction across the entire AI platform.
Technical Skills Required
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Benefits & Perks
Competitive salary & equity options
Sign-on bonus
Health, Dental, and Vision
401k
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Nice to Have
Experience deploying and operating LLMs and generative models in production at enterprise scale.
Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code.
Experience optimizing GPU clusters, scheduling, and distributed training frameworks.
Prior startup experience or comfort operating with ambiguity and high ownership.
Experience working with data engineering, feature pipelines, or real-time ML systems.
Job Description
Location: Cambridge, MA (Eastern Time / UTC -4)
Relocation package available or Remote option for Out-Of-State applicants
Start Date: ASAP
Languages: English (required)
About The Role
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