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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Technical Lead - **Company:** Next Step Systems - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Python (Programming Language), Machine Learning, Software Deployment, Pytorch, Backend, Production Code, Machine Learning Operations, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://www.dice.com/job-detail/15dbb09f-77ce-4f13-adf5-29f3abb78faa ## About the Role Experience building or shipping real Machine Learning systems used by people, not just demos. - Artificial Intelligence (AI) experience required. - Experience working with large models and understanding their failure modes. - Experience writing strong, production-grade code. - You are self-directed, pragmatic, and take full ownership of outcomes. - You communicate clearly and collaborate well in small, high-trust teams. - Tech Stack: GPU-based training and inference system, JAX, Python, and PyTorch. ## Description As Machine Learning Technical Lead, you own the execution layer of intelligence. You will translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You will be responsible for making models trainable, deployable, observable, and performant under real-world constraints. This position is 100% Remote. MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT. Machine Learning Technical Lead Responsibilities: - Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. - Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. - Architect and operate scalable inference systems, balancing latency, cost, and reliability. - Design and maintain data systems for high-quality synthetic and real-world training data. - Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. - Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. - Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. - Make pragmatic trade-offs and ship improvements quickly, learning from real usage. - Work under real production constraints: latency, cost, reliability, and safety Machine Learning Technical Lead Outcomes: - Research and models reliably translate into production-ready solutions with clear performance and quality targets. - ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. - Production issues are detected, debugged, and resolved quickly, minimizing user impact. - Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. - Iterations on models and systems are measurable, safe, and improve user experience over time., Looking to hire a Machine Learning Technical Lead in San Francisco, CA or in other cities? Our IT recruiting agencies and staffing companies can help., We help companies that are looking to hire Machine Learning Technical Leads for jobs in San Francisco, California and in other cities too. Please contact our IT recruiting agencies and IT staffing companies today! ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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