Staff ML Systems Engineer, Post-Training

Handshake; United States; Híbrido; USD 150 000 - 190 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity in a fast-growing company

401(k) match

Parental leave

Wellness stipend

Learning stipend

Free lunch & gym in SF office

Flexible PTO

Referral bonuses

Resumo da oferta

Handshake Labs, part of Handshake, seeks a Member of Technical Staff, Post-Training in the SF Bay Area to define and build post-training stacks and reusable infrastructure. You will collaborate with researchers and domain experts to turn insights into reliable systems and products.

You will shape evaluation frameworks, data pipelines, and production-quality implementations, with broad influence on our technical direction and culture.

Qualificações

• 3+ years in post-training, fine-tuning, or model-evaluation work.
• Strong Python skills and scalable software delivery.
• Hands-on experience with ML tooling, PyTorch, and large-scale data.
• Sound experimental judgment: hypotheses, metrics, diagnostics.
• Experience designing scalable ML systems with tradeoffs.

Responsabilidades

• Design post-training systems and methodologies for frontier models.
• Translate research needs into hypotheses, experiments, and production-quality implementations.
• Build and improve evaluation frameworks, data pipelines, and quality-control systems.
• Run fast iteration loops: prototype, evaluate, and apply learnings to new systems.
• Partner with researchers to develop high-signal data and evaluation methods.
• Identify repeatable patterns and productize them into reusable platforms.
• Raise the technical bar through design judgment, code quality, and mentorship.
• Contribute to benchmarks, open-source tools, and technical writing.

Conhecimentos

Post-training / model evaluation

Python

PyTorch

Experimentation & hypothesis

System design & scalability

Communication & collaboration

Descrição da oferta de emprego

Handshake Labs, part of Handshake, seeks a Member of Technical Staff, Post-Training in the SF Bay Area to define and build post-training stacks and reusable infrastructure. You will collaborate with researchers and domain experts to turn insights into reliable systems and products.

You will shape evaluation frameworks, data pipelines, and production-quality implementations, with broad influence on our technical direction and culture.