Lead Research Engineer

We are seeking a highly skilled Lead Research Engineer to lead optimization efforts for training and inference workloads running on Lightning AI infrastructure. This role sits at the intersection of ML systems, AI infrastructure, performance engineering, and practical research. You’ll drive improvements across models, inference systems, and platform infrastructure to improve performance, scalability, and reliability for real‑world AI workloads. This is a highly cross‑functional role that combines deep technical leadership with hands‑on implementation.

Successful candidates are comfortable operating broadly across the stack — from model behavior and inference systems to distributed infrastructure and developer tooling — while partnering closely with customers and internal engineering teams to solve complex AI systems challenges at scale. This role is based in one of our hubs (NYC, SF, London, or Seattle — NYC and London are preferred), with a minimum of 2 in‑office days per week and occasional team and company off‑sites.

What You'll Do

• Lead optimization efforts for large‑scale training and inference workloads across GPUs, accelerators, and distributed systems.
• Partner directly with customers to analyze workloads, identify bottlenecks, and drive improvements in performance, scalability, and reliability of deployed AI systems.
• Architect and improve inference pipelines, model serving systems, and performance‑oriented tooling for production AI workloads.
• Lead the design and implementation of profiling, debugging, and observability tools to analyze model execution and guide optimization strategies.
• Drive performance improvements across the software stack through clean APIs, automation, and seamless integration with the Lightning ecosystem.
• Collaborate cross‑functionally with infrastructure, product, and research teams to shape technical direction and improve the developer and user experience for AI workloads running on Lightning.
• Partner with hardware vendors and ecosystem partners to support efficient execution across diverse compute backends (NVIDIA, TPU, and emerging accelerators).
• Contribute technical leadership to open‑source projects through new features, tooling improvements, documentation, and community engagement.
• Stay current with advancements in large‑scale inference, distributed training, and ML systems optimization, and help guide adoption of new technologies and approaches.

What You’ll Need

Required Qualifications

• Strong expertise with deep learning frameworks such as PyTorch.
• Significant experience working with large‑scale training or inference workloads.
• Strong understanding of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling, distributed inference).
• Strong software engineering fundamentals, including designing APIs, building tooling, debugging complex systems, and shipping production‑quality code.
• Experience leading or driving performance optimization efforts across ML systems, infrastructure, or distributed workloads.
• Hands‑on experience with inference optimization techniques such as quantization, mixed precision, speculative decoding, memory‑efficient training, or throughput/latency optimization.
• Experience with modern ML systems and inference tooling such as TensorRT, vLLM, SGLang, Dynamo, Triton, DeepSpeed, or related technologies.
• Excellent collaboration and communication skills, including the ability to partner directly with customers, cross‑functional teams, and external contributors.
• Ability to operate effectively in ambiguous, fast‑moving environments and drive technical direction across multiple layers of the stack.
• Master’s or PhD in Computer Science, AI, Machine Learning, Systems, Engineering, or a related field.

Nice‑to‑Haves

• Experience contributing to or leading open‑source ML, infrastructure, or AI systems projects.
• Experience working closely with hardware vendors or accelerator ecosystems.
• Startup experience or experience operating in highly cross‑functional environments.
• Experience mentoring engineers or leading technical initiatives across teams.

Compensation

We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits. The anticipated annual base salary range for this role is: $225,000—$275,000 USD.

Benefits and Perks

We offer a comprehensive and competitive benefits package designed to support our employees’ health, well‑being, and long‑term success. Benefits may vary by location, team, and role. Benefits include:

• Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.).
• Retirement and financial wellness support (U.S.); Pension contribution (U.K.).
• Generous paid time off, plus holidays.
• Paid parental leave.
• Professional development support.
• Wellness and work‑from‑home stipends.
• Flexible work environment.

At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.