ML Engineer

Responsibilities

• Own evaluation pipelines — design, build, and automate offline and live evals that keep our speech and multimodal models honest in production.
• Harness the data — create tooling for safe, versioned, privacy-aware dataset curation and discovery.
• Ship models, not slide decks — partner with research and infra to prototype, train, and deploy state-of-the-art voice models that power Sesame’s real-time companion experience.
• Squeeze silicon — scale training and inference for LLM-class workloads; chase latency, throughput, and cost until the graphs flatten.
• Wire up monitoring and live evals — surface quality regressions before users or PMs notice.
• Move at startup speed — take ideas from whiteboard to production in days, not quarters; leave a clean trail of tests and dashboards behind.

Required Qualifications

• Expert-level PyTorch.
• Proven software engineer who loves ML; comfortable writing production code across the stack.
• Hands-on experience training or fine-tuning large language or other large-scale models with a variety of techniques.
• Evaluation expert — you’ve designed metrics and harnesses that actually predict user happiness.
• Deep knowledge of the ML lifecycle: dataset ops, training pipelines, eval frameworks, deployment, and monitoring.
• History of shipping complex projects to production—especially user-facing, online ML systems—despite shifting requirements and surprise roadblocks.
• High agency and the judgment to know when to sprint solo vs. pull in the squad.
• Track record of setting technical direction, driving consensus, and partnering smoothly with product, infra, and research.

Benefits

• 401 (k) max employer match: 3.5% of compensation
• 100% employer-paid health, vision, and dental benefits for you and your dependents
• Unlimited PTO and sick time
• Flexible spending account with employer matching up to $1,650/year (medical FSA)
• Guardian Employee Assistance Program (EAP)
• Opportunity to share in the company's success with competitive stock options