Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.
Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.
Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.
Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.
Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.
Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.
location: Cambridge, Massachusetts
job type: Permanent
work hours: 9am to 5pm
education: Bachelors
responsibilities:
- Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.
- Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.
- Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.
- Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.
- Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.
- Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.
qualifications:
3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models - architecture, training, and inference.
Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
Familiarity with modern software engineering practices.
You document experiments clearly and communicate trade‑offs crisply.
Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
At Randstad Digital, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact HRsupport@randstadusa.com.
Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad Digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).
This posting is open for thirty (30) days.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
