Data & AnalyticsRemote

Machine Learning Engineer

10a Labs · Remote

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Last seen by MeritLog September 12, 2026Source: GreenhouseSource version: greenhouse-job-board-v1

MeritLog read this listing from 10a Labs's Greenhouse job board and last checked it on September 12, 2026.

Source: the employer's Greenhouse job board. Open the original listing for current details.

Job details

Work model: Remote
Salary: $130,000 – $200,000
Location: Remote
Occupation: Computer Systems Engineers/Architects(O*NET 15-1299.08)

Hiring context

How this role compares at 10a Labs

10a Labs has 15 live roles in MeritLog’s catalog across 4 job families, and 7 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.

10a Labs concentrates this hiring in:

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What the role asks for

What you'd do

Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems.
Develop and evaluate models across reinforcement learning, NLP/LLMs, computer vision, and multimodal ML.
Build evaluation pipelines, benchmarks, datasets, and metrics for frontier AI systems.
Train, fine-tune, and evaluate models for safety, security, and other high-impact applications.
Develop reliable tooling and infrastructure to run ML experiments and evaluations at scale.
Analyze results, identify model failure modes, and translate findings into new experiments and technical approaches.
3–5+ years of experience in machine learning, research engineering, or a related technical field.
Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
Hands-on experience training, fine-tuning, or evaluating modern ML models.
Strong understanding of experimental design, model evaluation, and quantitative analysis.
Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents.
Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems.

What they're asking for

Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques.SkillPreferred
Experience evaluating frontier language or multimodal models.SkillPreferred
Experience with adversarial evaluations, robustness testing, or AI safety.SkillPreferred
Experience with distributed training, cloud ML infrastructure, or large-scale ML systems.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely. About the Role We are seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems across AI safety and model evaluation applications. This role combines strong ML engineering with an experimental mindset. You will work on problems involving reinforcement learning, model evaluations, language models, multimodal systems, and classifiers, taking ambiguous technical questions and turning them into rigorous experiments and scalable systems. You will collaborate closely with engineers, analysts, red teamers, and subject-matter experts supporting leading AI organizations. What You'll Do • Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems. • Develop and evaluate models across reinforcement learning, NLP/LLMs, computer vision, and multimodal ML. • Build evaluation pipelines, benchmarks, datasets, and metrics for frontier AI systems. • Train, fine-tune, and evaluate models for safety, security, and other high-impact applications. • Develop reliable tooling and infrastructure to run ML experiments and evaluations at scale. • Analyze results, identify model failure modes, and translate findings into new experiments and technical approaches. What We're Looking For • 3–5+ years of experience in machine learning, research engineering, or a related technical field. • Strong Python skills and experience with ML frameworks such as PyTorch or JAX. • Hands-on experience training, fine-tuning, or evaluating modern ML models. • Strong understanding of experimental design, model evaluation, and quantitative analysis. • Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents. • Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML. • Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems. Nice to Have • Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques. • Experience evaluating frontier language or multimodal models. • Experience with adversarial evaluations, robustness testing, or AI safety. • Experience with distributed training, cloud ML infrastructure, or large-scale ML systems. We don't expect candidates to have experience across every area above. We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques. Compensation & Benefits • Salary Range: $130K–$200K, depending on experience and location • Bonus: Performance-based annual bonus • Professional Development: Support for conferences, continuing education, or leadership training • Work Environment: Fully remote, U.S.-based • Health Benefits: Comprehensive health, dental, and vision coverage • Time Off: Generous PTO and paid holiday schedule

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