Senior ML Engineer, Optimization

Position Overview

Key Responsibilities

• Develop and execute hardware-aware post-training methods for full model quantization.
• Investigate preconditioning and formulate quantization as non-convex, discrete, constrained, or second-order optimization and develop practical solutions.
• Contribute to refining Neurophos's quantization strategy.
• Design controlled numerical experiments to understand potential improvements and secondary effects due to analog processing hardware.
• Build research-quality implementations and reproducible experiment harnesses for testing candidate methods.
• Adapt models from open-source repositories and customer private models.
• Work with models in various formats, including PyTorch, Triton, JAX, and emerging frameworks.
• Design and execute re-quantization, retraining, and other model adaptation techniques to minimize accuracy loss during precision reduction.
• Optimize GEMM operations for high-throughput execution.
• Collaborate with hardware, software, and architecture teams to co-optimize model architectures for optical compute characteristics.
• Publish research papers on novel optimization techniques and methodologies, with appropriate IP protection.

Qualifications

• PhD, or equivalent research experience, in machine learning, applied mathematics, optimization, numerical analysis, computer science, or a closely related field
• 5+ years of experience in machine learning engineering, with at least 3 years focused on model optimization and deployment.
• Research or advanced engineering experience in neural network quantization, model compression, numerical optimization, or efficient inference.
• Strong knowledge of numerical linear algebra, including matrix factorizations, conditioning, covariance estimation, and iterative methods.
• Experience with one or more of non-convex optimization, discrete optimization, manifold optimization, second-order methods, or constrained optimization.
• Strong proficiency in PyTorch and familiarity with other ML frameworks, including JAX, Triton, and TensorFlow.
• Hands-on experience with transformer architectures, LLMs, and diffusion models.
• Experience designing controlled numerical experiments and distinguishing algorithmic improvements from calibration or benchmark artifacts.
• Strong written communication and research collaboration skills.

Preferred Skills

• Experience with low-precision inference optimization (INT8, FP8, or lower).
• Background in analog or optical computing architectures.
• Knowledge of in-memory computing paradigms and matrix-vector multiplication acceleration.
• Knowledge of randomized numerical linear algebra, sketching, or structured transforms.
• Publications in quantization, optimization, numerical linear algebra, model compression, or efficient ML.
• Experience with vector quantization, lattice methods, learned codebooks, or rate-distortion ideas.
• Experience with large-scale batch inference optimization.
• Familiarity with prefill versus decode optimization strategies in LLM inference.
• Experience conducting experiments on models large enough to expose scaling and generalization problems.

What We Offer

• This is an opportunity to play a pivotal role in an innovative startup redefining the future of AI hardware. Work on game-changing technology at the intersection of photonics and AI as part of a collaborative, brilliant team. You’ll contribute to a platform that redefines computational performance and accelerates the future of artificial intelligence. Come help us bring this transformative technology to the world.

Benefits

• Join a team that invests in your future and your well-being. At Neurophos, we offer:
• 100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.
• Unlimited PTO. No rigid vacation banks, just a focus on delivery.
• 401(k) matching and stock option opportunities to ensure our success is your success.
• Full suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.
• Personalized Benefits. Choose the plans that fit your life and take the cash back for those that don’t.