Location: San Mateo, CA (Peninsula) – Onsite preferred, open to Bay Area candidates willing to commute
Salary / Hourly Rate: $110,000 - $175,000 per year
Benefits: US citizen/visa only,Competitive salary and meaningful equity options.,Comprehensive medical, dental, and vision coverage.,Catered team lunch in the San Mateo office.,Unlimited / flexible time off.,High-ownership role on a small, senior founding team (YC-backed).,Opportunities for rapid growth and learning building AI-powered CAD.
Role Information
Role Overview: N/A
Responsibilities: Design, train, and iterate on custom deep learning models that understand CAD workflows and predict high-quality next-step suggestions., Build and maintain robust Python training and evaluation pipelines, including data preprocessing, experimentation, and offline/online metrics., Architect model serving and backend components so that features are fast, reliable, and easy to integrate into CAD environments., Work closely with the founder and early customers (mechanical / hardware engineers) to understand real-world workflows and translate them into ML formulations., Own the full lifecycle of ML features — from research and prototyping through productionization, deployment, and monitoring., Collaborate with the broader engineering team on core product and infrastructure work (backend, APIs, data models, performance)., Establish best practices for experimentation, logging, and model comparison to ensure steady improvements over time., Stay current on relevant ML research (e.g., sequence models, geometric deep learning, representation learning) and decide pragmatically what is worth applying.
Qualifications: Deep ML expertise – 4+ years of hands-on machine-learning experience (or equivalent research / thesis-based Master’s or PhD), with a track record of training and improving deep models, not just using pre-built APIs., Strong Python engineering – you write clean, well-structured, production-ready Python without handholding, including tests, documentation, and thoughtful abstractions., Modern deep learning frameworks – expert with PyTorch (preferred) or similar frameworks such as TensorFlow / JAX; comfortable implementing and modifying custom architectures, loss functions, and training loops., End-to-end ownership – experience owning ML systems from data to deployment: building training pipelines, running experiments at scale, tuning hyperparameters, and shipping models into real products., Applied problem-solving – proven ability to take messy, open-ended product requirements and turn them into concrete ML formulations, experiments, and shipped features., Collaboration & communication – able to work closely with founders, engineers, and (eventually) customers; can explain trade-offs and model behavior clearly to both technical and non-technical partners., Startup mindset – comfortable in a fast-moving, low-process environment; willing to wear multiple hats across research, engineering, and backend work when needed., Nice to have – experience with geometry / graphics / CAD, 3D representations, or robotics; familiarity with cloud ML platforms (AWS / GCP) and backend frameworks (Flask, FastAPI, Django).
Minimum Requirements: Based in the US with existing work authorization; Hestus requires US citizen/visa only for this role.,Demonstrated experience training and shipping custom models (beyond simple “wrapper” or RAG work).,Strong proficiency in Python and at least one deep-learning framework (PyTorch preferred).,4+ years of industry or equivalent academic experience working on machine-learning systems.
Screening Questions: (Optional Video). This step is completely optional. If you’d like, record a short 2–3 minute video introducing yourself and your experience — or share a recording of your interview with the recruiter if that’s easier. You can upload the link via Loom or Google Drive. This just helps us get to know you better, but there’s no pressure if you’d prefer to skip it.,(Optional Portfolio / GitHub) If available, please share a link to your GitHub, portfolio, or any recent projects you’ve worked on. This is entirely optional but helps provide more context about your work.,Tell us about a machine learning project where you trained or significantly improved a model (not just using an off-the-shelf API). What was the problem, how did you design the model and data pipeline, and what measurable impact did your work have (e.g., accuracy, latency, or user experience)?
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: Step 1 – Short survey. After reviewing resumes, Hestus sends a short online questionnaire (≈15 minutes) to candidates who meet the basic requirements., Step 2 – Initial call. A 30–45 minute video call over Google Meet to learn more about your background, interests, and what you’re looking for, and to share more about Hestus, the role, and how they work., Step 3 – Take-home coding & ML exercise. A practical coding challenge (target ≤ 8 hours) focused on Python, problem-solving, and ML fundamentals relevant to Hestus’s work., Step 4 – Onsite / virtual technical interviews. A series of deeper technical interviews with the CEO, CTO, and engineering team. Candidates in the Bay Area are invited to the San Mateo office; others join via Google Meet. These sessions cover ML system design, research thinking, and production engineering., Step 5 – Offer & closing. For candidates who pass the loop, Hestus discusses compensation details, equity, and start date, and moves quickly to a formal offer.
Day to day: You’ll spend most of your time designing, training, and shipping models that make CAD feel “autocomplete-fast” for mechanical engineers. Some days will be deep in experiments, debugging optimization issues, and improving datasets; other days you’ll be pairing with the founder to profile performance, polish product behavior, or visit a customer to watch how they actually design. It’s a mix of research, engineering, and product work with very short feedback loops.
Team: You’ll report directly to the founder and work alongside a small group of highly capable engineers. The team is flat and hands-on — everyone ships code, reviews experiments, and contributes to product decisions. You’ll play a central role in defining Hestus’ ML stack and will be the go-to person for anything model-related.
Growth: Define the roadmap for CAD-focused ML research at Hestus., Mentor future ML and backend hires., Grow into a Staff / Principal IC or early engineering leader as the team scales.
Ideal Candidate Profile: Fundamental ML builder who cares about both theory and production. You enjoy reading papers, but you’re happiest when those ideas are running in production and making users faster. You write clean Python, understand how to debug models, and can own a feature from idea to deployed service. You’re excited by the idea of spending the next several years making one very hard problem — AI-powered CAD — world-class.
