10a Labs
via Greenhouse
Machine Learning Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 103d ago
The date the source published, not the day we noticed it (2026-06-03). Last seen at its source 1h ago.
Is it remote?
Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
No source stated where this role may be worked. This is read from the ad's own words.
What the ad says
…U.S.-based U.S.-based Health Benefits : Comprehensive health, dental, and vision…
Pay not stated
Similar roles pay $160k–225k/yr
Middle 50% of 2161 listings that do state pay — Engineering · all levels · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
greenhouse employer's own board first seen 11d ago · last seen 1h ago
- location not stated
The listing
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