{"id":3969264,"url":"https://alion.io/job/axiomatic-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":3841554,"name":"Axiomatic","domain":"axiomatic.ai","url":"https://alion.io/company/axiomatic-3","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":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States","Cambridge, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Poetry","optional":false},{"name":"Python","optional":false},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Pydantic AI","optional":true},{"name":"Sentry","optional":true}],"status":"live","first_seen_at":"2026-11-09T13:14:44Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T20:52:33Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About us\nAxiomatic AI is building a new class of AI systems designed to reason with the rigor of the scientific method. By combining deep learning with formal logic and physics-based modeling, we create verifiable, interpretable AI systems that collaborate with and support human researchers in high-stakes scientific and engineering workflows.\nOur mission, 30×30, is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030.\nWe aim to revolutionize hardware design and simulation in these industries and are building a team of highly motivated professionals to bring these innovations from research into commercial products.\nPosition overview\nWe are looking for an Applied AI Engineer to own the agent platform that lets AI agents run scientific and engineering workflows reliably and safely. Some of these workflows run in lab environments where reliability and safety matter as much as speed.\nYou will join a small, cross-functional team and be the main owner of the agent platform code. You will scope your own work and make the calls on how it is built.\nWhat you will do\nOwn the agent platform end to end: design, implementation, releases and support.\nDesign the typed, tested tools that agents use to plan, run and verify work, and decide what belongs in a tool and what belongs in a prompt.\nKeep the platform portable and well documented, so it is easy to adopt and extend.\nBuild safety and traceability into the platform from the start.\nRun evals that drive decisions: your own golden task sets, baselines for every release, and comparisons across clients and models, including local ones.\nShip changes in small, reversible steps, with upgrade paths that need no manual work from users.\nTurn research experiments into production features, and work with scientists to bring their workflows into the product.\nWrite ADRs and review code with the rest of the team.\nKey requirements\n5+ years of software engineering, mostly in companies shipping software to real customers. Research or academic experience is welcome, but it should not be your only background. Software engineers who moved into AI are a good fit.\n2+ years building LLM features that run in production at a company, beyond prototypes.\nStrong Python: packaging with uv or Poetry, type hints, async, testing and building CLIs.\nHands-on experience building or extending MCP servers and designing tools for LLM tool calling.\nGood working knowledge of current agent harnesses (Claude Code, Codex, Cursor or similar): plugins, skills, hooks, subagents, MCP support, and where each one falls short. You use them every day in your own work.\nA working understanding of agent architecture: how the model, the harness, tools and state fit together, and which decisions belong in code instead of a prompt.\nExperience running evals on LLM systems: building test sets, catching regressions and explaining the results to others.\nA safety mindset: human-in-the-loop approvals, guardrails in code, audit logs and awareness of prompt injection.\nCare for compatibility: versioning, smooth upgrades and installers you can run twice without breaking anything.\nCI/CD with GitHub Actions and Docker.\nComfort in a small team with unclear requirements. You can scope your own work and ship it with little support.\nClear, confident communication with both technical and non-technical stakeholders, including the ability to challenge or push back constructively when needed.\nProfessional fluency in English.\nNice to have\nExperience running LLMs and software in restricted or customer-managed environments.\nPydantic AI, LangGraph or similar agent frameworks.\nLab automation, instrument control or hardware connections (VISA/SCPI, OpenTAP).\nObservability tooling (OpenTelemetry, Logfire, Sentry).\nExposure to photonics or semiconductors.\nOpen-source contributions to agent tooling.\nWhat we offer\nCompetitive Compensation: A competitive compensation package.\n\nStock Options Plan: Empowering you to share in our success and growth.\n\nCutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.\n\nWork-Life Balance: A hybrid work arrangement with three days per week in our Boston office.\n\nProfessional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.\n\nImpact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.\n\nWork model & location expectations\nHybrid from our Boston office, 3 days per week.\nWhy join us?\nAt Axiomatic AI, you will be working on technology that drives innovation in AI for scientific and engineering applications in line with our 30×30 mission.\nThis is your opportunity to contribute to the development of new AI architectures that can reason coherently and produce interpretable and verifiable solutions, and to see those ideas commercialized into products that will shape the future of hardware and computing, while collaborating with a global team of engineers and AI specialists.\nWe believe in pushing the boundaries of what is possible and continuously seek to redefine the intersection of AI, with a focus on formal consistency. If you're ready to take your expertise in artificial intelligence and physics to the next level, we want to hear from you!\nWorried about not meeting every qualification? Studies show that women and people of color are less likely to apply for jobs unless they meet every listed requirement. At Axiomatic AI, we are dedicated to creating a diverse, inclusive, and authentic workplace. If this role excites you but your background doesn’t perfectly match every qualification, we still encourage you to apply. 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