Senior Machine Learning Engineer

About the company

Our client is an artificial intelligence company.

The role

Raydar is recruiting for this role on behalf of our client. Design and build reinforcement learning environments, evaluation systems and grading methods for technical design work, along with the data pipelines and automation that feed model training. The role suits a machine learning engineer with hardware-adjacent domain knowledge who can turn expert task data into reliable training assets.

What you'll do

• Build reinforcement learning environments and evaluations for computer-aided design tasks in mechanical and electrical engineering.
• Develop scoring logic and reward functions that judge the quality of technical output dependably.
• Turn expert task data into curated datasets and environments for model training.
• Build data pipelines and ML infrastructure that move data from collection through to training use.
• Automate manual scoring and content creation workflows.
• Work with domain engineers who define correct outcomes and with teams using the environments for post-training.
• Run experiments that inform technical direction and product priorities.

What we're looking for

• 3 to 6 years of full-time experience in machine learning or applied AI.
• Prior work in a physical-engineering setting, day-to-day partnership with mechanical or electrical engineers, or a degree in one of those fields.
• Some exposure to engineering design software or vision-based machine learning applied to physical objects.
• Hands-on experience building reinforcement learning environments, or using them for post-training.

Bonus points

• Senior-level machine learning depth, with a core focus on reinforcement learning, data pipelines, evaluations and LLM-as-judge methods rather than primarily computer vision or supervised fine-tuning.
• Practical skill building model assessments, scoring systems, data flows and training infrastructure.
• A track record of high initiative and ownership, including taking the right next step beyond assigned work.
• Experience at a scaling startup comfortable with ambiguity.

Location and work model

• San Francisco, CA, United States
• On-site, in office
• Full-time

Compensation, as the posting describes it

• Equity