--- title: Member Technical Staff Post Training at Handshake description: Handshake is hiring for the Member Technical Staff Post Training role in San Francisco, CA. See the full description and apply. type: job url: https://www.foundrole.com/jobs/member-technical-staff-post-training-at-handshake-01a0c5af-82e5-719d-8b19-5de40809cc2c date: 2026-09-28T04:26:24Z og_description: Join Handshake as Member Technical Staff Post Training in San Francisco, CA. Pays $200K–$350K per year. Full-time, hybrid role. og_image: https://www.foundrole.com/og/snj5ba.png breadcrumbs: - label: Home url: https://www.foundrole.com/ - label: Search url: https://www.foundrole.com/jobs --- | | | |---|---| | **Company** | Handshake | | **Location** | San Francisco, CA (Hybrid) | | **Salary** | $200K/yr - $350K/yr | | **Type** | Full Time | | **Posted** | Sep 21, 2026 | ## Remote check **Fully remote: passed our check.** The role itself is remote, with no recurring office days, travel under 10%, no on-site duties and no end date on the remote setup. **Where you can work from:** Not stated in the posting. Ask the recruiter before you apply. ## Description # **About Handshake** Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We've grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month. ## **About Handshake Labs** Handshake Labs is building external AI products, research platforms, and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment. Our work spans the full post-training loop: designing evaluations and training environments, building high-quality data and feedback systems, running experiments, and turning what works into durable infrastructure. For example, we are developing agents that can analyze long, complex coding-agent sessions in days rather than weeks—with expert review and calibration built into the system. ## **The Role** We are hiring a Member of Technical Staff, Post-Training to help define and build this new organization. This is a broad, high-ownership role for researchers who build. You may come from research science, research engineering, machine learning engineering, or a closely related background; what matters is the ability to reason deeply about model improvement and turn that reasoning into reliable systems. You will partner with researchers, domain experts, and customers to turn ambiguous post-training questions into experiments, evaluation frameworks, data pipelines, and products. Early members of the team will have unusual influence over our technical direction, operating culture, and the reusable systems we build. We care more about demonstrated research capability, technical judgment, and a builder’s mindset than a specific title, degree, or career path. **Location:** San Francisco & Mountain View preferred; *we are open to exceptional candidates in other locations.* ## **What you’ll do** - Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, and related approaches. - Translate open-ended research or partner needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations. - Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems. - Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product. - Partner directly with AI researchers and domain experts to develop high-signal data, feedback, and evaluation methods. - Identify repeatable patterns across engagements and productize them into reusable software and platforms. - Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship. - Contribute to the field through benchmarks, open-source tools, research, and technical writing where it creates leverage. ## **What we’re looking for** - 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work. Relevant experience may include RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments. - Strong Python skills and the ability to write clean, efficient, scalable software. - Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows. - Sound experimental judgment: you can form hypotheses, choose meaningful metrics, diagnose failures, and distinguish signal from noise. - Experience designing systems—not only implementing specifications—including the ability to make tradeoffs around quality, scale, reliability, and reuse. - Comfort operating in an ambiguous, fast-moving environment with substantial ownership. - Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers. ## **Especially compelling experience** - Building or operating large-scale ML training, inference, data, or evaluation systems. - Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks. - Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems. - Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems or AI research. - Experience productizing research or repeated customer work into robust, reusable platforms. ## **Why join** - Work on problems at the center of how frontier AI systems improve, alongside leading labs and domain experts. - Help build an early technical organization where your work shapes the roadmap, standards, and culture. - Move fluidly from research insight to real-world systems, with the resources and customer context to see those systems matter. - Join a company building durable infrastructure for careers in the AI economy. ## **Perks** Handshake delivers benefits that help you feel supported—and thrive at work and in life. The below benefits are for full-time US employees. 🎯 Ownership: Equity in a fast-growing company 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend 📚 Growth: $2,000 learning stipend, ongoing development 💻 Office: Commuting support, free lunch, and gym in our SF office 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days 🤝 Connection: Team outings & referral bonuses ## Skills - Supervised Fine-Tuning (Sft) - Synthetic Data Generation - Technical Leadership - Data Annotation - Open-Source Software - Quality Control - Python - Code Quality - Data Protection Officer - Machine Learning Model Fine-Tuning - Artificial Intelligence - Technical Writing - Big Data - PEFT (Parameter-Efficient Fine-Tuning) - Mentoring - Reinforcement Learning from Human Feedback (Rlhf) - Large Language Models (Llm) - Data Quality - Reinforcement Learning - Human-in-the-Loop - Communication - Reward Modeling - Machine Learning - Data Processing - LoRa ## Benefits - Mental Health - Health and Wellness Stipends - Health and Wellness Programs - Health Insurance - Dental Insurance - Vision Insurance - Parental Leave - Paid Time Off (Pto) - 401(k) Plans - Company Events - Food and Meals ## How to Apply [Apply for this position](https://www.foundrole.com/jobs/member-technical-staff-post-training-at-handshake-01a0c5af-82e5-719d-8b19-5de40809cc2c?utm_source=ai_markdown) ## Related - [Browse all companies](https://www.foundrole.com/companies?utm_source=ai_markdown)