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
Act as a technical partner to ML research, data engineering, and product teams - translating needs into platform priorities
Communicate roadmap, incidents, and technical tradeoffs clearly to both engineers and senior leadership
Help ML teams become self-sufficient on the platform, reducing bottlenecks on the platform team itself
Team Building & Management
Actively participate in hiring to grow the team from 4 to ~10–12 engineers, including defining roles and leveling
Mentor and develop engineers, establishing a team culture early that will hold as headcount scales
Define lightweight but durable team processes - on-call rotations, incident response, and engineering standards that won't need to be rebuilt at scale
Be comfortable doing IC work yourself while simultaneously building the team's capacity to take it on
What We're Looking For
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)
The base pay range for this position is between $198,000.00 to $300,000.00 annually. Base pay will depend on multiple individualized factors, including, but not limited to, internal equity, job-related knowledge, skills, and experience. This range represents a good-faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental, vision, 401(k), paid time off, and an annual bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer for employment.
