The job, in the employer's words
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.
Research Areas
Infrastructure Intelligence: Models that help us understand what’s happening in infrastructure and why. Detecting anomalies, forecasting issues, analyzing telemetry across logs, metrics, events, and traces, understanding causality, and connecting runtime behavior back to code. These capabilities form the foundation our agents use to reason about infrastructure.
Autonomous Agents: Long-running, parallel agents that detect, diagnose, and remediate infrastructure issues, including fixing code and configuration. Advancing multi-step reasoning, orchestration, context management, memory, and reinforcement learning.
Evaluation: Making sure agents work well and informing how we improve. Partnering with platform to build evaluation methodology, generate synthetic data, analyze historical incidents, and model the domain.
About Antimetal
Antimetal is building the future of infrastructure management. We're starting by creating a platform that investigates, resolves, and prevents issues—giving engineers their time back to focus on what they do best: building great products.
What You’ll Do:
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. Build the tooling that lets the team measure progress and iterate with confidence.
Explore Research Directions: Apply and develop techniques from best-in-class AI Agents, ML, and SRE research to our problem domain. Experiment with new approaches to reasoning, retrieval, codebase mapping, and agent architectures.
Collaborate Across Teams: Work with platform and product to integrate capabilities and productionize prototypes into scalable and reliable services.
What you bring:
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.
Bonus:
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.
Who you are:
Identify as a builder.
Are excited to work in-person from our new and spacious office in New York.
Love working in a startup environment (experience in a startup or obsession with going zero-to-one).
Enjoy working with people who are ambitious, caring, and think in systems.
Thrive in a fast-paced iterative environment where experimentation is essential.
What we bring:
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.
Commute perks — Citi Bike + train benefits.
Interview process
Application Review – Send us your stuff, and a quick note on why you're excited.
Intro Chat: Share what you're looking for next and learn more about what we're building.
Founder Interview: Talk with one of our founders in more detail about the role
Technical Interview: We’ll have you complete a short exercise specific to the role.
Onsite: Come onsite and meet the team through a series of 1:1 interviews.
Decision – We’ll move fast.
Requirements mentioned
- 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.
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