{"id":176634,"url":"https://alion.io/job/vizcom-machine-learning-data-engineer","title":"Data Engineer, Machine Learning","company":{"id":63578,"name":"Vizcom","domain":"vizcom.com","url":"https://alion.io/company/vizcom","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":84,"open_postings":4,"ghost_share":0,"stale_share":0.5,"repost_share":0.25,"time_to_fill_p50_days":49,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":163000,"max":208000,"currency":"USD","period":"year","gross":null,"usd_annual":208000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Multimodal AI","optional":true},{"name":"SOC 2","optional":true}],"status":"live","first_seen_at":"2026-08-18T05:25:21Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-16T22:09:13Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":2,"flags":["repost"],"days_open":58},"description":"Data Engineer, Machine Learning\nSan Francisco, CA · In Person · Full-Time\nApplying to this role will also allow us to consider you for other research opportunities at Vizcom. We believe the best roles are shaped around exceptional people, not just job descriptions.\nAbout Vizcom\nVizcom is where design teams at companies like Nike, GM, New Balance, and Hasbro bring ideas from sketch to product. Designers use Vizcom to sketch, render, explore color and materials, work in 3D, and prepare concepts for production.\nThe render itself was never the point. The point is the physical thing that comes after it. We call this pencil to product.\nVizcom is a Series B company with more than $52M raised.\nMore than 700,000 designers have worked in Vizcom, and every session leaves a trail: candidates selected, outputs promoted into designs, regions masked and renamed, and entire directions kept or discarded.\nThat trail is one of the most valuable things we create outside of the product itself. Today, though, it's more archaeology than asset. Only a fraction of what happens in a session reaches training-grade quality, while increasingly sophisticated post-training methods depend on exactly this kind of high-quality, domain-specific data.\nYour job will be to help turn that trail into a machine.\nA design session isn't a simple sequence - it's a branching tree. Designers fork, backtrack, iterate, and abandon entire directions on their way to the thing they ultimately keep. The judgment lives in the shape of that process, and today we capture only pieces of it.\nThe Role\nAs an ML Data Engineer, you'll build and improve the systems that turn professional design work into training-grade preference data and training results back into a better product.\nYou'll work alongside the researchers consuming what you build, within the product systems where these signals originate, and across the data infrastructure where they ultimately land. Your closest users are the researchers sitting beside you, and you'll see quickly when a dataset you've built allows them to ask a question they couldn't ask before.\nThis is not a support role, and it isn't traditional offline ETL.\nThe pipelines you work on will run through a live product used every day by professional design teams, including enterprise customers with rigorous expectations around privacy and data protection. Capturing better signals without compromising user trust, contractual obligations, or product performance is a core part of the work.\nIf you want to train models without building the systems that feed them, this probably isn't the role for you. If you believe the next advances in ML will increasingly be won through better data, it might be.\nWe think about a dataset as a product: it has users, versions, provenance, and a quality bar. A training result should be reproducible from a dataset fingerprint months later, and \"Where did this example come from?\" should always have an answer.\nHere, helping build that standard is the job.\nWhat You'll Own\nThe capture surface: Partner with Product and Engineering to improve what the product records and help design and ship instrumentation in production systems.\n\nThe data pipeline: Build and maintain the path from canvas to warehouse to training set, ensuring data is clean, versioned, reliable, and reproducible.\n\nDataset contracts and lineage: Implement systems that make examples traceable to their origin and training datasets reproducible over time.\n\nPrivacy and data boundaries: Build systems that reflect what can be captured and used under different enterprise agreements, with consent, isolation, and appropriate safeguards designed in from the beginning.\n\nResearch-ready datasets: Create appropriately governed and sanitized datasets that allow researchers to experiment safely and effectively.\n\nCollection instruments: When historical signals aren't enough, help build mechanisms for gathering explicit feedback that designers actually want to use.\n\nHonest representations of ambiguity: Design data systems that preserve context. Unpicked doesn't necessarily mean disliked, abandoned doesn't necessarily mean rejected, and our data should reflect the difference.\n\nData quality: Build checks, monitoring, and tooling that make it easier to identify gaps, inconsistencies, and unexpected changes before they affect research or training.\n\nThis is a charter, not a week-one checklist. We don't expect one person to tackle everything at once. You'll work with the broader team to prioritize the areas where you can have the most impact and expand your scope over time.\nWhat Your First 90 Days Could Look Like\nDays 1-30: Map\nUnderstand the event surface, warehouse, existing datasets, research workflows, and current data boundaries. Identify what exists, what's missing, and which research questions our data can't yet answer.\nGet comfortable with the existing pipeline and begin contributing improvements to data quality, reliability, or observability.\nDays 30-60: Ship\nTake one new signal end to end: instrumented in the product, landed reliably in the warehouse, versioned appropriately, and available to researchers.\nDocument the implementation and validate that the resulting dataset behaves as expected.\nDays 60-90: Build the standard\nOwn a meaningful improvement to how we version, validate, trace, or govern ML data and contribute to the standards the team uses for dataset fingerprints, lineage, quality, and data boundaries.\nIdentify follow-up opportunities based on what you've learned and begin taking ownership of a broader area of the ML data stack.\nWhat We're Looking For\nExperience building data infrastructure, ML data systems, or production data pipelines used by other teams.\n\nStrong software engineering skills and experience building reliable production systems.\n\nExperience designing data models and pipelines with reproducibility, observability, and lineage in mind.\n\nComfort working with large or complex datasets and debugging issues across multiple parts of a data system.\n\nAn experimental mindset and comfort working closely with researchers to turn ambiguous questions into measurable datasets.\n\nGood judgment around data quality, including an understanding of when the absence of a signal is meaningfully different from a negative signal.\n\nComfort operating in an environment where the underlying systems and standards are still evolving.\n\nA collaborative approach to engineering, including documenting decisions, incorporating feedback, and working across Product, Engineering, and Research.\n\nNice to Have\nYou've trained models yourself and understand what ML training pipelines actually need from their data.\n\nYou've worked with preference data, labeling systems, human-feedback pipelines, or evaluation operations.\n\nYou've built data infrastructure under meaningful privacy, security, or contractual constraints.\n\nYou've worked with large-scale event or behavioral datasets.\n\nYou have experience with data systems supporting generative AI or multimodal models.\n\nAbove all, we're looking for someone who can look at the exhaust of a complex product and see evidence.\nWhat You'll Get\nA unique dataset: The recorded decisions and workflows of more than 700,000 designers, with new signals generated every day.\n\nResearchers as your users: You'll work directly alongside the people using the datasets you build, creating an unusually tight feedback loop between data engineering and research.\n\nMeaningful ownership: You'll own substantive pieces of Vizcom's ML data infrastructure and have room to expand your scope as you build context and expertise.\n\nA rare problem space: High-quality professional preference data is difficult to create. You'll have the opportunity to build systems around a dataset and domain that few ML teams have access to.\n\nDirect access to the founders: You'll work closely with Vizcom's founders and technical leadership as we build out our research and ML infrastructure.\n\nBenefits at Vizcom\n100% employer-sponsored medical coverage for employees, plus 25% coverage toward dependents\n\nDental and vision coverage, plus mental health benefits\n\nMeaningful equity ownership\n\nFlexible PTO\n\n401(k) with employer match\n\nGenerous annual Learning & Development allowance\n\nPaid parental leave\n\nWeekly catered lunch at our San Francisco headquarters\n\nMonthly gym membership stipend\n\nCompensation\nBase salary for this role is $163,000 - $208,000 + equity\nWe regularly benchmark compensation against relevant peer companies using current market data from industry-standard sources, including Carta and Pave. This range reflects our Tier 1 compensation market, which includes San Francisco.\nThe actual offer and overall compensation package will be determined based on multiple factors, including relevant experience, skills, qualifications, and business considerations. The compensation and benefits described in this posting apply to U.S.-based W-2 employees and may vary based on applicable employment laws and requirements.\nHow We Work\nWe document what we learned, not just what we worked on.\n\nNegative results are valuable when they help us close off the wrong paths.\n\nResults should be reproducible before they earn additional compute.\n\nWe share meaningful research through technical write-ups, demonstrations, and showcases where appropriate.\n\nOur interview process emphasizes real-world problem solving and practical technical work rather than LeetCode-style interviews.\n\nLocation\nThis is an in-person role based in San Francisco, CA.\nJoin Us\nAt Vizcom, we move quickly, give people meaningful ownership, and offer the opportunity to shape both our product and our company as we grow. We believe deeply in the craft of industrial design and in building tools that help designers bring better ideas into the physical world.\nJoin us in shaping a world designed by you.\nA Note to Candidates and Recruiting Agencies\nPlease apply directly through this job posting. To help us keep our hiring process fair and organized, we ask candidates not to contact Vizcom employees directly regarding their application or candidacy.\nVizcom is not seeking assistance from external recruiting agencies or search firms for this role. Please do not contact or solicit Vizcom employees regarding recruiting services, candidate submissions, or agency partnerships. Unsolicited resumes or candidate profiles submitted by agencies will not create a fee obligation on behalf of Vizcom.\nAs part of Vizcom's SOC 2 Type II compliance program, employment is contingent upon successful completion of a background check, as permitted by applicable law.","description_format":"text","description_chars":10589,"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","Gym membership","Parental leave"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial Design","Generative Image"],"lifecycle":[{"event":"open","at":"2026-08-30T16:12:27Z"},{"event":"close","at":"2026-09-12T17:12:51Z"},{"event":"reopen","at":"2026-09-17T23:16:07Z"},{"event":"close","at":"2026-10-06T16:54:07Z"},{"event":"reopen","at":"2026-10-08T00:52:11Z"}],"visa":[],"liveness":{"score":21,"band":"cold","label":"Long shot","p_open":1,"p_active":0.385,"p_room":0.55,"age_days":59,"expected_fill_days":49,"reasons":["conf:1","repost:2","win:tail"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":208000,"is_top_pay":false},"html_url":"https://alion.io/job/vizcom-machine-learning-data-engineer","json_url":"https://alion.io/job/vizcom-machine-learning-data-engineer.json","meta":{"generated_at":"2026-10-16T23:55:46Z","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":6097,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"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":63578},"rest":"https://alion.io/mcp/rest/get_company?id=63578"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fvizcom-machine-learning-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"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%2Fvizcom-machine-learning-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"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%2Fvizcom-machine-learning-data-engineer"},{"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/vizcom-machine-learning-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fvizcom-machine-learning-data-engineer"}]}