{"id":3270157,"url":"https://alion.io/job/cobalt-staff-ai-engineer","title":"Staff AI Engineer","company":{"id":171764,"name":"Cobalt","domain":"cobalt.io","url":"https://alion.io/company/cobalt-2","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":200000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Structured Outputs","optional":false},{"name":"GCP","optional":true},{"name":"Go","optional":true},{"name":"Kubernetes","optional":true},{"name":"Wi-Fi","optional":true}],"status":"live","first_seen_at":"2026-10-26T11:08:32Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-11-01T12:54:27Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Description\nCobalt 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.\nYou’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.\nYour 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.\nWhat You’ll Do\nSet technical direction for agent systems across teams, including orchestration, tool use, context and memory management, human oversight and failure recovery.\nDesign, build and operate shared foundations: evaluation frameworks, agent observability and tracing, execution controls, and model access with routing and failover where needed.\nEstablish 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.\nMake agent workflows resilient to tool failures, timeouts, rate limits and partial execution, with clear limits on retries, runtime and spend.\nWork with security and platform teams to build in scoped permissions, customer data isolation, audit trails and safe execution of tools and code.\nEstablish 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.\nLead model selection and cost optimization. Benchmark models against real workloads and make informed trade-offs between quality, latency and cost.\nPartner with teams on production readiness, incident investigation and improvements to agent reliability.\nHelp teams use AI coding agents effectively across planning, implementation, review and testing. Measure the impact on delivery speed and software quality.\nMentor engineers and technical leads through design reviews, pairing, internal talks and written guidance. Help teams adopt shared capabilities and improve them based on feedback.\nWork with product, research and security leaders to turn business priorities into a technical roadmap with clear outcomes.\nYou Must Have\nSignificant software engineering experience, typically 8-10+ years, with a track record of technical leadership across teams.\nHands-on experience building and operating LLM-based agents in production, with a clear understanding of their scale, constraints and business outcomes.\nA track record of improving agent reliability: building evaluations, investigating non-deterministic failures and improving success rates against measurable goals.\nStrong knowledge of agent architectures, including tool use, orchestration, planning, context and memory management, and the judgment to know when simpler workflows are sufficient.\nExperience running LLM workloads through managed cloud model platforms or direct provider APIs, including rate limits, failure handling and cost tracking.\nStrong backend and distributed systems fundamentals, including APIs, asynchronous workflows, concurrency and fault tolerance.\nExperience with production observability, incident investigation and safe rollout of system changes.\nAn understanding of security risks in agent systems, including prompt injection, unsafe tool execution and exposure of sensitive data.\nEvidence of impact beyond your own code: shared platforms other teams adopted, standards that improved outcomes or engineers you helped grow.\nTools and Practices You’ve Likely Used\nWe care more about your depth of experience and engineering judgment than experience with any specific tool.\nAgent orchestration: Framework-based or custom orchestration, tool calling and structured outputs.\n Tool integration: Model Context Protocol (MCP), APIs and permission-controlled tool execution.\nEvaluations: Automated evaluation harnesses, domain-specific test datasets and regression testing in CI.\nObservability: Distributed tracing, structured logs and metrics for multi-step agent workflows.\nModel access: Managed cloud model platforms, direct provider APIs and multi-provider gateways.\nNice To Have\nExperience with GCP, Go or Kubernetes.\nExperience building sandboxed environments for agents that execute untrusted code or interact with external systems.\nOffensive security, application security or security tooling experience.\nExperience introducing AI coding tools across engineering teams and measuring their impact.\nPublic writing, talks or open-source contributions related to agent engineering.\nWhy You Should Join Us \nGrow in a passionate, rapidly expanding industry operating at the forefront of the Pentesting industry \nWork directly with experienced senior leaders with ongoing mentorship opportunities\nEarn competitive compensation and an attractive equity plan\nSave for the future with a 401(k) program (US) or pension (EU) \nBenefit from medical, dental, vision and life insurance (US) or statutory healthcare (EU)\nLeverage stipends for:\nWellness\nWork-from-home equipment & wifi\nLearning & development\nMake the most of our flexible, generous paid time off and paid parental leave \nPay Range Disclosure \nCobalt is committed to fair and equitable compensation practices. The salary range for this role is ($200,000 - $240,000) + 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. \nCobalt (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.\nCobalt 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). 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