Research Engineer

SuperAnnotate

Research Engineer conducting AI research at SuperAnnotate, exploring machine learning methods and building innovative solutions within a hybrid work environment.

Posted 7/21/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSenior💰 $180,000 - $280,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in machine learning methodologies, including model training and evaluation, with a strong ability to translate research into practical applications. Proficient in Python programming and capable of independently managing research projects from conception to execution.

Highest-signal resume keywords

MS Or PhD In ML, CS, Or Related FieldHands-On Experience With RL/Agentic SystemsStrong Python Programming SkillsTechnical Writing SkillsExperience In AI/ML Evaluation And Benchmarking

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel TrainingModel EvaluationReimplementation Of MethodsResearch ImplementationData AnnotationExperiment DesignBenchmarkingMultimodal MLEvaluation Harnesses

Soft Skills

High AutonomyClear Communication

Tools & Technologies

Python

Industry Keywords

Research ExperienceQuantitative FieldTechnical Feasibility AssessmentCustomer Dataset DevelopmentInternal Dataset Creation

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Benefits

Comp & perks

In addition to the annual base salary, employees are eligible for an annual bonus paid out quarterly.