Senior ML Engineer, Optimization

Neurophos
Austin, Sunnyvale
Workplace: OnsiteFull timeUSD 160,000 - 215,000 annuallyFunction: Data Science & Machine LearningExperience: 5+ yearsEducation: phdSkills: ["Written communication","Research collaboration","Numerical optimization","Experiment design","Hardware-aware modeling"]

Develop hardware-aware post-training quantization and optimization methods for large language models, diffusion models, and other workloads running on Neurophos optical inference engines. Bridge ML research with low-precision, hardware-constrained execution by formulating quantization as non-convex/discrete optimization, building reproducible experimental harnesses, and implementing research-quality PyTorch/JAX/Triton workflows. Collaborate across hardware, software, and architecture teams to improve accuracy and throughput while publishing results.

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Neurophos
Neurophos
2 days ago

Senior ML Engineer, Optimization

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Last checked: 2 hours agoStatus: Live

Job Summary

Develop hardware-aware post-training quantization and optimization methods for large language models, diffusion models, and other workloads running on Neurophos optical inference engines. Bridge ML research with low-precision, hardware-constrained execution by formulating quantization as non-convex/discrete optimization, building reproducible experimental harnesses, and implementing research-quality PyTorch/JAX/Triton workflows. Collaborate across hardware, software, and architecture teams to improve accuracy and throughput while publishing results.
Location: Austin, Sunnyvale
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Develop and execute hardware-aware post-training quantization methods for full model quantization.
  • •Investigate preconditioning and formulate quantization as non-convex, discrete, constrained, or second-order optimization.
  • •Design controlled numerical experiments to measure improvements and effects from analog processing hardware.
  • •Build research-quality implementations and reproducible experiment harnesses; adapt models from open-source and customer private models.
  • •Adapt and re-quantize/retrain models to minimize accuracy loss and optimize GEMM operations for high-throughput execution.

Pay and Benefits

Salary: USD 160,000 - 215,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceHsa Contributions401kEquityPaid LeaveDentalVisionLife InsuranceCritical IllnessAccident Insurance

Key Requirements

  • •PhD, or equivalent research experience, in machine learning, applied mathematics, optimization, numerical analysis, computer science, or a closely related field.
  • •5+ years of 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 (e.g., matrix factorizations, conditioning, covariance estimation, iterative methods).
  • •Experience with non-convex/discrete/constrained optimization or second-order methods, plus strong PyTorch proficiency and familiarity with JAX and Triton.
Experience:5+ yearsMachine learningOptimizationNumerical analysisQuantizationEfficient inference
Education:PhD / Doctorate
Skills:Written communicationResearch collaborationNumerical optimizationExperiment designHardware-aware modeling
Tech Stack:PyTorchTritonJAXTensorFlow

Company Brief

Neurophos
Develops metamaterial-based photonic optical processing units (OPUs) to deliver high-performance, energy-efficient AI inference chips for datacenters, aiming to scale photonic compute to exaflop levels.
Industry: Deep Tech
Company Size: Small (11 to 50 employees)
Growth: Growth Stage Startup
Funding: Series A
Headquarters: Austin, United States
Founded: 2020
WebsiteLinkedIn