{"id":4040815,"url":"https://alion.io/job/circuit-staff-engineer-core-ml","title":"Staff Engineer, Core ML","company":{"id":23506,"name":"Circuit","domain":"circuit.ai","url":"https://alion.io/company/circuit","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"KServe","optional":false},{"name":"Langfuse","optional":false},{"name":"LoRA","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reranking","optional":false},{"name":"Synthetic Data","optional":false},{"name":"vLLM","optional":false},{"name":"VLM","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-11-10T21:01:00Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T22:09:03Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Circuit\nCircuit is the AI execution layer for the physical economy.\nIndustrial and manufacturing enterprises run on expertise: the hard-won knowledge of how complex products are engineered, configured, sold, and supported. That expertise is scarce, slow to transfer, and walking out the door as a generation of experts retires. It lives in PDFs, spreadsheets, and the heads of a few people, and the hardest technical decisions wait on whoever happens to know.\nCircuit puts expert judgment in everyone's hands. We capture how the best people make critical technical decisions and turn it into agents that do the judgment-heavy work, so anyone, regardless of skill level, can make the right call, from quote to install and support, with the accuracy these environments demand.\nCircuit is built by operators. Our leadership team has built and scaled real industrial companies, and we are already live in production with category-leading manufacturers. We are not bolting AI onto an old playbook. We are building the layer that runs the last mile of how the physical economy executes.\nThe Role\nAs a Staff Engineer, Core ML at Circuit, you’ll join Product Engineering and own the ML capabilities at the center of our product: understanding complex technical documents, and keeping the long-running agents that work with them reliable.\nOur customers’ knowledge lives in blueprints, CAD files, spec sheets, and scanned PDFs that mix text, vector geometry, and raster content. You’ll build the models and algorithms that turn those documents into structured information, and the tools that let our agents use it. You’ll also improve the agent loop itself: how agents plan, choose tools, recover from failure, and behave consistently from one run to the next.\nMost of your time goes into the algorithms and models behind the product rather than individual customer projects. Product engineers and FDEs work closest to customers and bring you the failure patterns they see. You analyze how our models and agents perform, decide where better algorithms, data, or training will pay off, and set the evaluation bar that tells us whether a change worked. You’ll join customer conversations when a problem needs your depth.\nThis is an applied role, not a research one. You’ll adapt and combine proven approaches, measure them against real customer data, and ship them. Your teammates range from full-stack engineers to applied ML researchers, and success is measured by what reaches production, not papers published.\nWhat Is in Production Today\nOur core product includes a long-running agent loop written in Go. Frontier model APIs drive the loop, while specialized tools handle retrieval, extraction, and agent execution. These tools let us solve industrial problems that general-purpose agent harnesses struggle with.\nWe use a combination of open-source and internally fine-tuned models, particularly vision-language models (VLMs), hosted on KServe. Our customer data includes technical documents, high-resolution blueprints, raster images, vector geometry, CAD, and structured data.\nKey Responsibilities\nDevelop algorithms that extract meaning from complex technical documents containing text, vector geometry, CAD, and raster content, spanning vision models, geometric algorithms, and language understanding.\n\nBuild those document understanding capabilities into the tools that power Circuit's long-running agentic systems.\n\nImprove the reliability, determinism, and effectiveness of agentic systems, including agent planning, tool use, and failure recovery.\n\nBuild and extend training and evaluation pipelines for our core models and agents, so every change is measured against real customer data before it ships.\n\nAnalyze how our algorithms perform in production, find where and why they fail, and turn that into the next improvement.\n\nFind gaps and bias in our datasets, and close them through curation, labeling, or synthetic data.\n\nTrack new research and techniques, and make the promising ones work in production.\n\nMentor and elevate other engineers on the team.\n\nExamples of Work You Could Own\nFix a failure class. Trace why extraction breaks on a class of drawings, such as dense electrical schematics, fix it with better data, a better model, or a better algorithm, and show the lift in the evals.\n\nFine-tune a specialized model. Fine-tune a LoRA on an in-house model to extract a specialized artifact from a high-resolution blueprint page.\n\nBring a new algorithm to production. Take an approach from a recent paper, such as a new layout or table-structure model, prove it beats what we run today on our data, and make it fast and reliable enough to ship.\n\nImprove retrieval. Improve retrieval quality, relevance, or latency across document types and customers.\n\nMake agents more deterministic. Find why the same task produces different results from run to run, and change the tools or the loop so it stops.\n\nExperience\nWe want someone who has shipped ML-powered software to production, can adapt proven approaches and evaluate alternatives from other production ML systems, and measures whether a change works for customers. Ideally, you have deep expertise and strong technical opinions in at least one of these areas:\nAgent harness design and execution\n\nDocument extraction and structured data understanding\n\nHigh-resolution computer vision\n\nVLM fine-tuning and inference\n\nRetrieval systems, including embeddings and reranking\n\nAgent evaluation and optimization\n\nYou do not need to be an expert in every area. Strong software engineering fundamentals and the ability to lead technical decisions matter more than familiarity with our exact stack. Beyond that, we look for:\nHands-on AI/ML engineering experience, with a track record of shipping models in production.\n\nExperience training and fine-tuning models for production, including the datasets and pipelines behind them.\n\nExperience building and operating ML evaluation pipelines, including datasets, metrics, and regression testing for models and agents in production.\n\nExperience profiling data distributions, identifying gaps and bias, and curating high-quality datasets for training and evaluation.\n\nExperience with vision and multimodal models, especially on technical documents (PDFs, CAD) with text, vector geometry, and raster content.\n\nStrong knowledge of data structures, algorithms, and software engineering principles.\n\nProven expertise with LLMs, deep learning systems, and applied ML.\n\nExperience debugging production ML systems using AI observability tools (e.g. Langfuse, Opik, etc.), tracing failures to root cause.\n\nProficiency in Python and relevant ML frameworks (PyTorch, vLLM, etc.).\n\nDaily, fluent use of AI coding tools. You write effective prompts, iterate when output is wrong, and reliably tell working code from code that only looks right.\n\nBonus: Computational geometry, or experience with vector drawing formats such as CAD and vector PDFs.\n\nBonus: Go, the language our agent loop is written in.\n\nBonus: Familiarity with construction, civil engineering, or related industries where interpreting complex technical drawings and specifications is central to the work.\nHuman Skills\nOwnership mindset with a focus on delivering working systems in production.\n\nStrong product judgment, with an understanding of how system behavior impacts user experience and trust.\n\nRigorous reviewer of agent instructions, skills, and documentation, ensuring they’re clear, unambiguous, and reliable for agents to act on.\n\nCuriosity about how systems behave in practice and how customers use them.\n\nBias toward shipping. You take ambiguous problems and drive them to working solutions without needing the path fully defined for you.\n\nJudgment about what is ready to ship, and the willingness to push back to keep the work grounded in customer outcomes.\n\nClear communicator. You explain ML tradeoffs to engineers, FDEs, and product stakeholders in plain terms, and share progress early.\n\nCritical evaluation of AI output. You spot hallucinated APIs, incorrect logic, and subtle bugs before they reach production.\n\nSystems thinking, including attention to correctness, determinism, and failure modes in long-running agents.\n\nRigor about measurement. You measure before you claim an improvement, and you can tell a real gain from noise in an eval.\n\nRaises the bar for the team through design reviews, code reviews, and mentoring.\n\nLow ego, with a focus on team outcomes.\n\nWhat We Offer\nHigh-caliber team of seasoned manufacturing and AI leaders\n\nHelp create a category-defining platform transforming how manufacturers sell, support, and innovate\n\nCompetitive compensation, meaningful equity, and employer-paid benefits\n\nFlexible PTO and access to the latest AI tools\n\nA values-driven culture that rewards ownership, curiosity and velocity","description_format":"text","description_chars":8805,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","Intranet & Knowledge Management"],"lifecycle":[{"event":"open","at":"2026-11-10T22:09:03Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":40,"reasons":["conf:2","win:early"],"computed_at":"2026-11-11T00:24:45Z"},"pay":null,"html_url":"https://alion.io/job/circuit-staff-engineer-core-ml","json_url":"https://alion.io/job/circuit-staff-engineer-core-ml.json","meta":{"generated_at":"2026-11-11T00:24:45Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":876,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-12T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":23506},"rest":"https://alion.io/mcp/rest/get_company?id=23506"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcircuit-staff-engineer-core-ml"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcircuit-staff-engineer-core-ml"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcircuit-staff-engineer-core-ml"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/circuit-staff-engineer-core-ml\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcircuit-staff-engineer-core-ml"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4040815}},{"tool":"search_jobs","arguments":{"company":"circuit.ai","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4040815},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}