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$180k–$280k/yr
Full-time
postgraduate degree
Annual Bonus
Posted 21d ago
~40 hrs/week
Responsibilities
The Research Engineer will independently lead research directions, reimplementing relevant methods and building MVPs to validate ideas. They will turn ambiguous requirements into concrete outputs such as customer datasets, pilots, or published research papers.
Requirements
Requires an MS or PhD in ML, CS, or a related field, or equivalent demonstrated research experience. Candidates must possess deep ML knowledge, strong Python engineering skills, and experience with RL, multimodal ML, or AI benchmarking.
Full job description
About SuperAnnotate
SuperAnnotate helps the world’s leading AI teams build responsible, next-generation models powered by high-quality human data. We’re a fast-growing Series B startup bridging the gap between advanced AI innovation and the data that drives it. Our global network of expert specialists, scalable managed operations, precise talent matching, and full project transparency ensure unmatched data quality at scale. Trusted by innovators like Databricks and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate is proud to be the top-ranked AI data company on G2 for multiple consecutive years, including 2025.
The Impact You'll Make
Our 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.
\nWhat 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.
A deep RL background specifically.
\n
$180,000 - $280,000 a year
In addition to the annual base salary, employees are eligible for an annual bonus paid out quarterly.
\n
Why SuperAnnotate
This is a rare opportunity to work at the intersection of frontier AI research and real production impact. You'll work on projects with frontier labs that move the needle on model performance, with your work feeding directly into the next generation of agent capabilities. You'll have the opportunity to implement projects that actually matter, publish research, and present at conferences – alongside a multidisciplinary, multinational team and collaborate with some of the most prominent labs and AI companies globally.
Only shortlisted candidates will be contacted for an interview!
Equal Opportunity
We are an equal-opportunity employer and value diversity at our company. At SuperAnnotate diversity means to us making an effort to reflect the many experiences and identities of the outside world, and treating each other with fairness and without bias. Every day we foster an environment where people of all backgrounds not only belong, but excel to succeed as a company and grow together. We offer equal opportunity regardless of sex, sexual orientation, national origin, color, race, age, marital status, disability, gender identity, veterans and more.
SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thousands of rigorously vetted experts, ethical and scalable managed operations, precise talent matching, and purpose‑built technology, SuperAnnotate delivers full project visibility and unmatched data quality. SuperAnnotate powers complex annotation, evaluation, and reinforcement learning workflows to build, evaluate and align frontier AI.
Trusted by innovators like Databricks, IBM and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate enables the world’s top AI teams to build responsible and state‑of‑the‑art models with human data.
Offices: 100 Bush St, Suite 410, San Francisco, California 94104, US · Hanrapetutyan, 59, Yerevan, 0010, AM · Lindstedtsvägen, 24, Stockholm, 10044, SE
Artificial IntelligenceSoftwareGenerative AISaaSNatural Language ProcessingData Collection and LabelingArtificial Intelligence (AI)Computer VisionMachine Learning
How much do Science & Research jobs in California pay?
Based on 11638 listings with disclosed salaries, most science & research jobs in California pay between $103k–$250k per year. Individual offers vary with seniority, company size, and specialization.
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There are currently 18,450 open science & research positions in California listed on Clera. New openings are added daily as companies post roles.
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Companies currently hiring include Applied Materials, Northrop Grumman, Apple, Google, Northrop Grumman Mission Systems, Inc., among others. Browse the listings above to see every active employer.
Are there remote or hybrid Science & Research jobs in California?
Yes — 3908 of the 18450 open science & research positions offer remote or hybrid work (1009 remote, 2899 hybrid).
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