Member of Technical Staff – Post-Training

Handshake

. Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, and related approaches .

Posted 9/23/2026full-timeRemote • United States, Canada, United KingdomLead💰 $200,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing post-training systems and methodologies for machine learning models, with a strong focus on evaluation frameworks and data-processing pipelines. Proven ability to collaborate with researchers and domain experts to translate complex needs into actionable experiments and production-quality solutions.

Highest-signal resume keywords

Post-Training Systems DesignPython ProgrammingPyTorch ExperienceReinforcement LearningModel Evaluation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Supervised Fine-TuningReinforcement LearningReward ModelingData-Processing PipelinesEvaluation FrameworksQuality-Control SystemsHypothesis FormationScalable Software DevelopmentLarge-Scale ML TrainingModel Behavior Analysis

Soft Skills

Collaborative CommunicationExperimental JudgmentMentorshipAdaptabilityProblem-Solving

Tools & Technologies

ML ToolingTraining EnvironmentsEvaluation WorkflowsOpen-Source ToolsAnnotation Systems

Industry Keywords

Machine LearningAI ResearchData Quality FrameworksHuman-in-the-Loop SystemsTechnical Leadership

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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 hypotheses, experiments, evaluation plans, and production-quality implementations
Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems
Run rapid iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product
Partner 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 design judgment, communication, code quality, and mentorship
Contribute through benchmarks, open-source tools, research, and technical writing where it creates leverage
Help define Handshake Labs' technical direction, operating culture, and reusable systems

Requirements

What you’ll need

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 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, including forming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise
Experience designing systems and making tradeoffs around quality, scale, reliability, and reuse
Comfort operating in an ambiguous, fast-moving environment with substantial ownership
Collaborative communication and ability to work with researchers, engineers, domain experts, and customers
Especially compelling: large-scale ML training, inference, data, or evaluation systems
Especially compelling: LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks
Especially compelling: reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems
Especially compelling: published research, meaningful open-source contributions, or technical leadership in ML systems or AI research
Especially compelling: productizing research or repeated customer work into robust, reusable platforms

Benefits

Comp & perks

Equity in a fast-growing company
401(k) match
Competitive compensation
Financial coaching
Paid parental leave
Fertility benefits
Parental coaching
Medical, dental, and vision insurance
Mental health support
$500 wellness stipend
$2,000 learning stipend
Ongoing development
Commuting support
Free lunch
Gym in the San Francisco office
Flexible PTO
15 holidays + 2 flex days
Team outings
Referral bonuses