{"id":176635,"url":"https://alion.io/job/vizcom-research-engineer-post-training","title":"Research Engineer, Post-Training","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":164000,"max":215000,"currency":"USD","period":"year","gross":null,"usd_annual":215000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"SFT","optional":false},{"name":"SOC 2","optional":true}],"status":"live","first_seen_at":"2026-08-18T05:25:05Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-10-16T13:59:26Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":59},"description":"Research Engineer, Post-Training\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 from investors including Radical Ventures, Index Ventures, and Nat Friedman.\nFive years of professional designers working this way has created something difficult to reproduce in a traditional research environment: millions of moments where a trained designer, in the middle of real work, decided what should survive into a product that ultimately has to become real.\nThose decisions create a uniquely interesting research problem. A designer's preference among several candidates can reflect the generator's style, where they are in the design process, what they are trying to make, and the professional judgment they bring to the decision. Existing approaches don't cleanly separate those signals.\nUnderstanding that judgment - and learning how to model it - is the challenge this role will help solve.\nThe Role\nAs a Research Engineer, Post-Training, you'll work on models that help us understand and learn from the judgment that carries a design from pencil to product.\nYou'll work closely with the engineers building our post-training stack, contributing to experiments, model training, evaluations, and a growing body of research documenting the approaches we've tested, what we've learned, and where we've found meaningful signal.\nThis role sits directly between research and product. You'll have the opportunity to see the models you work on ship to working designers, while learning from the real-world signals generated by how those designers use Vizcom.\nIf your primary goal is research that ends with publication, this may not be the right environment. If you're excited by the idea of helping build models that influence what professional designers see in the product, it probably is.\nWe also believe the strongest results won't come from clever objectives alone. They'll come from excellent engineering: correct training code, rigorous evaluations, reliable pipelines, and experiments we can trust.\nWhat You'll Own\nExecute well-scoped post-training research and engineering projects, from experiment design through evaluation and implementation.\n\nTrain and evaluate reward and preference models using years of professional design decisions.\n\nExplore and apply methods including supervised fine-tuning, distillation, preference optimization, and reinforcement learning.\n\nDevelop rigorous evaluations that help us determine whether an experimental result is real, reproducible, and worth pursuing.\n\nWork closely with more senior research and engineering partners to translate promising research results into production systems.\n\nPartner with Product and Design to understand how models perform in real workflows and identify opportunities to improve them.\n\nEvaluate emerging post-training techniques and prototype approaches that may be useful within our stack.\n\nContribute to reliable training and experimentation infrastructure that makes it easier to run, compare, and reproduce experiments.\n\nDocument experiments, results, and learnings so the team can build on them over time.\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 team to prioritize the problems where you can have the most impact.\nWhat Your First 90 Days Could Look Like\nDays 1-30: Learn and map\nUnderstand our data, post-training stack, existing research, evaluation methods, and the approaches we've already tested. Get comfortable running experiments within the existing training and evaluation infrastructure.\nDays 30-60: Build and validate\nOwn a scoped research or engineering problem and produce an initial result using historical data that holds up against our evaluation and reproducibility standards.\nDays 60-90: Contribute and expand\nBuild on your initial work, identify promising follow-up experiments, and contribute to the team's roadmap for improving our post-training systems and connecting model behavior more closely to signals from designers using Vizcom.\nWhat We're Looking For\nStrong programming and software engineering skills, particularly for machine learning systems.\n\nHands-on experience training or fine-tuning machine learning models.\n\nExperience with generative models, including diffusion or flow models, through professional work, research, or substantial technical projects.\n\nFamiliarity with one or more post-training methods such as supervised fine-tuning, preference optimization, reward modeling, distillation, or reinforcement learning.\n\nExperience designing experiments and evaluating model performance.\n\nStrong fundamentals in machine learning and an ability to turn research ideas into working implementations.\n\nAn interest in product-coupled research, where research questions are informed by real users and models make their way into production.\n\nComfort working through technical problems where the answer isn't known in advance.\n\nA collaborative approach to research and engineering, including documenting results and incorporating feedback from others.\n\nNice to Have\nExperience building or contributing to high-performance training or inference systems.\n\nExperience optimizing ML workloads or working with large-scale training infrastructure.\n\nExperience working with preference data or human-feedback systems.\n\nExperience taking ML experiments from prototype toward production.\n\nAn interest in industrial design, physical products, or the people who make them.\n\nAbove all, we're looking for someone who is more interested in understanding how professionals decide than optimizing for what the internet likes.\nWhat You'll Get\nA unique dataset: Five years of professional design decisions, with new signals generated every day.\n\nResearch that reaches users: The professionals whose judgment you're modeling are also the people using Vizcom. Successful research can reach their workflows quickly.\n\nMeaningful ownership: You'll own substantive research and engineering problems while working alongside experienced teammates who can help you expand your scope over time.\n\nClose product feedback loops: Research insights can directly shape what Vizcom builds and what data we capture next.\n\nDirect access to the founders: You'll work closely with Vizcom's founders and technical leadership as we build out the research function.\n\nCompensation\nAnnual base salary: $164,000 - $215,000 USD + 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.\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\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.\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.\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.\nWe are not accepting outreach from recruiters on this role, and ask applicants to refrain from reaching out to our employees. 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