Boston Dynamics
Senior Engineering Manager, ML Platform
- Compensation
- $198K–$300K / yr
- Location
- Waltham Office (POST)
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 7–12 years of engineering experience, with at least 2–3 years in a formal management or tech lead capacity
- Demonstrated experience building or scaling a platform, infrastructure, or ML systems team from the ground up
- Technical credibility in one or more of: GPU/distributed compute infrastructure, large-scale data storage and retrieval, or data pipeline frameworks
- Experience making foundational architectural decisions in an early-stage or greenfield environment
- Strong cross-functional communication skills - able to translate between ML researchers, engineers, and senior leadership
- Comfortable with ambiguity; able to define the roadmap rather than just execute against one
- A hands-on mindset - willing and able to write code, review designs, and debug production issues alongside your team
Nice to have
- Familiarity with compute orchestration frameworks such as Kubernetes, Slurm, or Ray
- Experience with ML training workflows, dataset generation pipelines, or feature stores
- Prior experience growing a team through a hiring ramp (e.g. doubling or tripling headcount)
Full description
We're looking for a Senior Engineering Manager to lead our ML Platform Team - a growing team responsible for the foundational infrastructure that powers our machine learning work. This is a player-coach role: you'll set technical direction and contribute hands-on while building out the team and establishing the processes that will scale with it.
The platform is in its early stages, with some foundations in place. You'll be joining at a pivotal moment - making architectural decisions that will shape how the team and the platform grow from 4 engineers today to a team of 10–12.
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What You'll Work On
Infrastructure Leadership
- Own the strategy, roadmap, and execution for GPU compute infrastructure, ensuring it scales to meet growing model training and fine-tuning demands
- Contribute directly to infrastructure design and implementation, particularly in the near term as the team grows
- Drive reliability, performance, and cost efficiency across distributed training clusters. Optimize existing and new training workloads to achieve scale.
- Evaluate and adopt new hardware (GPUs, TPUs, custom accelerators) and cloud/on-prem infrastructure as the team's needs evolve
Data Platform Ownership
- Oversee the design and operation of data storage, indexing, and retrieval systems that support large-scale dataset generation
- Ensure data pipelines are performant, fault-tolerant, and meet the quality and freshness requirements of ML teams
- Establish early-stage standards for data access, lineage, and governance — pragmatic and scalable, not over-engineered
Shared Tooling & Developer Experience
- Lead the development and maintenance of shared libraries and frameworks for data transformation pipelines
- Partner with ML researchers and engineers to understand their workflows and translate them into reliable, reusable platform capabilities
- Champion developer productivity - reduce friction for teams consuming platform services
Technical Strategy & Architecture
- Lay the architectural foundations of the platform, making decisions that are pragmatic today but designed to scale to a 10–12 person team and beyond
- Make key architectural decisions around compute orchestration (e.g. Kubernetes, Slurm, Ray), storage systems, and pipeline frameworks
- Balance short-term delivery with long-term platform health -knowing when to build, buy, or borrow
Cross-functional Collaboration