Staff AI Engineer New

Cobalt; United States; Remote; USD 275,000 - 300,000; Full time

Benefits offered by this job

Equity plan

401(k) program (US)

Medical, dental, vision insurance

Job summary

Cobalt is seeking a Staff AI Engineer to lead production agent systems and set the technical direction for reliable, secure, and cost-efficient AI agents. You will mentor teams, design foundations for evaluation, observability and safe execution, and partner across engineering, product, research and security.

Your focus is the agent platform, production practices, and improving how teams build software with AI coding agents. This role blends hands-on work with strategic leadership.

Qualifications

• 8–10+ years of software engineering with leadership across teams.
• Hands-on experience building and operating LLM-based agents in production.
• Proven track record improving agent reliability and reducing failures.
• Strong knowledge of agent architectures: tool use, orchestration, context and memory.
• Experience running LLM workloads on managed cloud platforms or provider APIs.
• Strong backend and distributed systems fundamentals: APIs, asynchronous workflows, concurrency.
• Experience with production observability and incident investigation.
• Understanding security risks in agent systems and data isolation.
• Evidence of impact beyond code: shared platforms, standards, mentoring.

Responsibilities

• Set technical direction for agent systems across teams.
• Design, build and operate shared foundations: evaluation frameworks, agent observability and tracing, execution controls, and model access with routing and failover where needed.
• Establish evaluation practices that measure task completion, accuracy, security and cost per successful task; work with domain experts to build test cases.
• Make agent workflows resilient to tool failures, timeouts, rate limits and partial execution, with clear limits on retries and spend.
• Work with security and platform teams to build scoped permissions, data isolation, audit trails and safe execution of tools.
• Establish engineering standards: CI evals, prompt/model versioning, regression testing, cost and latency budgets, staged rollouts with rollback paths.
• Lead model selection and cost optimization; benchmark models against real workloads and trade-offs.
• Partner with teams on production readiness, incident investigation and improvements to agent reliability.
• Help teams use AI coding agents across planning, implementation, review and testing; measure impact on delivery speed and software quality.
• Mentor engineers and technical leads through design reviews and guidance.
• Work with product/research/security to turn business priorities into a technical roadmap.

Skills

Leadership

LLM agents in production

Agent architectures

Backend & distributed systems

Observability & incident response

Security in agent systems

Cost optimization

Tools

Go

Kubernetes

GCP

Job description

Cobalt runs AI agents in production that test real customer applications. We’re looking for a Staff AI Engineer who has built and operated production agent systems and can help us make them more reliable, secure and cost-efficient.

You’ll set the technical direction for how we design, evaluate, ship and operate agents, and stay hands-on building the foundations our teams use. You’ll work across engineering, product, research and security, mentoring engineers and helping teams turn promising prototypes into dependable production systems.

Your primary focus will be our agent platform and production practices. You’ll also help engineering teams improve how they build software with AI coding agents.

What You’ll Do

• Set technical direction for agent systems across teams, including orchestration, tool use, context and memory management, human oversight and failure recovery.
• Design, build and operate shared foundations: evaluation frameworks, agent observability and tracing, execution controls, and model access with routing and failover where needed.
• Establish evaluation practices that measure task completion, accuracy, false positives, security and cost per successful task. Work with domain experts to build representative test cases and turn production failures into regression tests.
• Make agent workflows resilient to tool failures, timeouts, rate limits and partial execution, with clear limits on retries, runtime and spend.
• Work with security and platform teams to build in scoped permissions, customer data isolation, audit trails and safe execution of tools and code.
• Establish practical engineering standards: evals in CI, prompt and model versioning, regression testing for non-deterministic systems, cost and latency budgets, and staged rollouts with rollback paths.
• Lead model selection and cost optimization. Benchmark models against real workloads and make informed trade-offs between quality, latency and cost.
• Partner with teams on production readiness, incident investigation and improvements to agent reliability.
• Help teams use AI coding agents effectively across planning, implementation, review and testing. Measure the impact on delivery speed and software quality.
• Mentor engineers and technical leads through design reviews, pairing, internal talks and written guidance. Help teams adopt shared capabilities and improve them based on feedback.
• Work with product, research and security leaders to turn business priorities into a technical roadmap with clear outcomes.

You Must Have

• Significant software engineering experience, typically 8–10+ years, with a track record of technical leadership across teams.
• Hands-on experience building and operating LLM-based agents in production, with a clear understanding of their scale, constraints and business outcomes.
• A track record of improving agent reliability: building evaluations, investigating non-deterministic failures and improving success rates against measurable goals.
• Strong knowledge of agent architectures, including tool use, orchestration, planning, context and memory management, and the judgment to know when simpler workflows are sufficient.
• Experience running LLM workloads through managed cloud model platforms or direct provider APIs, including rate limits, failure handling and cost tracking.
• Strong backend and distributed systems fundamentals, including APIs, asynchronous workflows, concurrency and fault tolerance.
• Experience with production observability, incident investigation and safe rollout of system changes.
• An understanding of security risks in agent systems, including prompt injection, unsafe tool execution and exposure of sensitive data.
• Evidence of impact beyond your own code: shared platforms other teams adopted, standards that improved outcomes or engineers you helped grow.

Tools and Practices You’ve Likely Used

We care more about your depth of experience and engineering judgment than experience with any specific tool.

• Agent orchestration: Framework-based or custom orchestration, tool calling and structured outputs.
• Tool integration: Model Context Protocol (MCP), APIs and permission-controlled tool execution.
• Evaluations: Automated evaluation harnesses, domain-specific test datasets and regression testing in CI.
• Observability: Distributed tracing, structured logs and metrics for multi-step agent workflows.
• Model access: Managed cloud model platforms, direct provider APIs and multi-provider gateways.

Nice To Have

• Experience with GCP, Go or Kubernetes.
• Experience building sandboxed environments for agents that execute untrusted code or interact with external systems.
• Offensive security, application security or security tooling experience.
• Experience introducing AI coding tools across engineering teams and measuring their impact.
• Public writing, talks or open-source contributions related to agent engineering.

Why You Should Join Us

• Grow in a passionate, rapidly expanding industry operating at the forefront of the Pentesting industry
• Work directly with experienced senior leaders with ongoing mentorship opportunities
• Earn competitive compensation and an attractive equity plan
• Save for the future with a 401(k) program (US) or pension (EU)
• Benefit from medical, dental, vision and life insurance (US) or statutory healthcare (EU)
• Leverage stipends for:
• Work-from-home equipment & wifi
• Make the most of our flexible, generous paid time off and paid parental leave

Pay Range Disclosure

Cobalt is committed to fair and equitable compensation practices. The salary range for this role is ($275,000 - $300,000) + equity + benefits. A candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications. The salary range may differ in other states and may be impacted by proximity to major metropolitan cities.

Cobalt (the "Company") is an equal opportunity employer, and we want the best available persons for every job. The Company makes employment decisions only based on merit. It is the Company's policy to prohibit discrimination in any employment opportunity (including but not limited to recruitment, employment, promotion, salary increases, benefits, termination and all other terms and conditions of employment) based on race, color, sex, sexual orientation, gender, gender identity, gender expression, genetic information, pregnancy, religious creed, national origin, ancestry, age, physical/mental disability, medical condition, marital/domestic partner status, military and veteran status, height, weight or any other such characteristic protected by federal, state or local law. The Company is committed to complying with all applicable laws and providing equal employment opportunities. This commitment applies to all persons involved in the operations of the Company regardless of where the employee is located and prohibits unlawful discrimination by any employee of the Company.

Cobalt is an E-Verify employer. E-Verify is an Internet-based system operated by the Department of Homeland Security (DHS) in partnership with the Social Security Administration (SSA). It allows participating employers to electronically verify the employment eligibility of their newly hired employees in the United States.