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We’re looking for a Research Engineer to build the intelligent systems that power Antimetal. You’ll prototype new approaches, run experiments, and own the path from research to production. You’ll work closely with platform and product to shape agent capabilities and contribute to evaluation methodology. Infrastructure, and its corresponding observability, is a hard domain to model. Telemetry is high-volume, noisy, and ephemeral. Ground truth is approximate. We’re building AI agents that understand this complexity and can reason about what’s happening, why, and how to fix it, including making changes to code and configuration.
Job Responsibility:
Experiment, Evaluate, Iterate, Ship: Run experiments across our research areas, analyze results, validate what works, and take successful approaches to production
Build Evaluation Infrastructure: Partner with platform on live and offline evaluation pipelines, benchmarks, and synthetic data generation
Explore Research Directions: Apply and develop techniques from best-in-class AI Agents, ML, and SRE research to our problem domain
Collaborate Across Teams: Work with platform and product to integrate capabilities and productionize prototypes into scalable and reliable services
Requirements:
4+ years of experience in applied ML, research engineering, preferably at a company shipping production AI systems
Production experience contributing to agentic/LLM systems, including multi-step reasoning, reinforcement learning, fine-tuning, and orchestration
Proven experience bringing work from prototype to production, using data and experimentation to drive product and architectural decisions
Strong on ML fundamentals: statistical modeling, probabilistic methods, time-series analysis, evaluation methodology
Real world expertise in one area of applied ML: search, statistical modeling, NLP, etc
Experience constructing and running end-to-end evaluation pipelines with real world data
Proficient in Python and Typescript, with experience using common ML libraries and data engineering tools
Strong problem-solving skills, with a focus on creating highly maintainable, scalable code
Comfortable with ambiguity and iterative development, prototyping, and adapting quickly to feedback
Nice to have:
Exposure to interpretability, robustness, or AI safety research
Experience with multimodal models (text + images, logs, or other data types)
Track record of contributions to ML research (open-source repos, papers, workshops)
Strong foundations in statistics, optimization, or experimental design
Experience deploying research models into production environments
What we offer:
Pay & ownership — Competitive salary with generous equity grants
Full coverage + retirement — Fully covered health, dental, and vision, plus retirement benefits
Unlimited PTO — Take the time you need to recharge
Dinner on late nights — Working late? Dinner is on us
Fitness stipend — Monthly support for your health and wellness
Tools of the trade — Any equipment you need to do your best work