Job Description

The Impact You'll MakeOur research team is expanding to keep pace with a wave of frontier-facing work: internal research streams, client engagements that require real ML depth, and emerging opportunities at the cutting edge of the field. As a Research Engineer, you'll take a research direction and run with it - finding the right papers, benchmarks, and prior work, reimplementing what's relevant, and building out the process to reproduce and improve on it internally.

You'll own initiatives end to end: partnering with strategic project and technical leads to scope the work, building MVPs to validate ideas (including through human annotation and agents), and turning that work into something concrete - a customer dataset, a pilot, an internal dataset that becomes a paper or blog post, or a joint publication with a partner. You won't be handed a fully specified task list; you'll be given a direction and the autonomy to turn it into a research plan.

This is a full-time, hybrid position based in San Francisco.

What You'll Do
• Take a research direction and independently identify supporting resources - papers, benchmarks, blog posts - then implement or reimplement the relevant methods
• Build and own the process to reproduce prior work internally and identify ways to improve on it
• Own projects (for example, an RL/agentic environment build for a partner or a novel multimodal benchmark) end to end, including scoping, MVP implementation, and validation
• Partner with strategic project leads and technical leads to translate ambiguous requirements into a concrete, testable research plan
• Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data
• Turn research directions into tangible outputs - a paid customer dataset, a customer pilot, an internal dataset, or a paper/blog post for publication or conference presentation
• Bring an ML perspective to new opportunities - assessing technical feasibility of incoming requests and helping shape proposals where research depth is needed

What You'll Bring
• MS or PhD in ML, CS, or a related quantitative field - or equivalent demonstrated research experience (publications, significant open-source research work, industry research)
• Real ML depth: you understand how models are trained and evaluated, not just how to call an API. You can read a paper, judge whether its claims hold, and reimplement the method
• Hands-on experience with at least one of: RL/agentic systems, AI/ML evaluation and benchmarking, or multimodal ML
• Strong Python and the engineering ability to build and ship your own experiments - eval harnesses, environments, infrastructure - without relying on a platform team
• High autonomy: you can turn an ambiguous direction into a concrete research plan and notice when something's off before being told
• Clear technical writing

Nice To Have
• Publication track record (first-author preferred)
• Experience with agent or multimodal benchmarks (OSWorld, MMMU, WebArena, SWE-bench, or similar) or building RL environments/gyms
• Familiarity with reward modeling, reward hacking, or verifier/judge reliability
• Familiarity with synthetic data generation or human-in-the-loop (HITL) workflows
• Experience with cloud infrastructure and containerized environments

Requirements

Have queries? We're here to assist you! Chat live with our recruiter or connect with the job owner directly:
Email:[email protected]

Embark on a rewarding journey together with us! At SA Technologies Inc., your journey begins with a friendly technical assessment, providing a stage to highlight your unique skills.