Onsite Forward-Deployed ML Engineer: Scale AI Experiments

Autoscience Institute; Menlo Park (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Unlimited PTO

Conference attendance and publication

Competitive compensation

Job summary

Autoscience Institute is seeking a Forward Deployed Engineer for an on-site, full-time role in Menlo Park, CA. You will work directly with customers to deploy AutoScience into their ML environments, turning ambiguous modeling problems into scalable experiments and production-ready solutions.

You will write production code, navigate real ML pipelines, and collaborate with research teams to continuously enhance the core product while owning deployments from first conversation to the first model

Qualifications

• Strong Python programming skills and solid software engineering fundamentals.
• Experience building, training, evaluating, or deploying ML systems.
• Experience with production cloud infrastructure and containerized workloads.
• Strong debugging skills across software, infrastructure, data, and ML systems.
• Experience owning technically complex projects from ambiguity to production.

Responsibilities

• Deploy AutoScience into customer environments and production ML pipelines.
• Write production code and work inside real ML pipelines.
• Debug distributed systems and reason about ML challenges.
• Collaborate with research and engineering to improve core product.
• Own deployments end-to-end from initial conversation to model improvement in production.

Skills

Python programming

ML deployment

Cloud infrastructure

Debugging

Project ownership

Technical communication

Customer collaboration

Autonomy

Distributed systems

Job description

Autoscience Institute is seeking a Forward Deployed Engineer for an on-site, full-time role in Menlo Park, CA. You will work directly with customers to deploy AutoScience into their ML environments, turning ambiguous modeling problems into scalable experiments and production-ready solutions.

You will write production code, navigate real ML pipelines, and collaborate with research teams to continuously enhance the core product while owning deployments from first conversation to the first model