As an ML Engineer specializing in inference, you will optimize models for production, implement neural network techniques, and collaborate with researchers.
About us:
The role:
What you’ll do:
You may be a strong fit if you:
Logistics:
At Phota Labs, we’re building visual GenAI that helps people capture, express, and relive their memories — in ways that feel effortless, personal, and emotionally resonant. Our core technology enables personalized image generation that faithfully reflects who you are and the moments you experienced. Our first goal is to bring visual GenAI into everyday photography.
We're a small team of researchers, engineers, and designers who have always been at the forefront of how people capture, edit, and share images and videos. We build with our hands and hearts. We believe GenAI is the next shift for photography, and are seeking builders who share this vision — people like us, like you. We're just getting started!
As our first ML Engineer specializing in inference and optimization, you'll bridge the gap between cutting-edge research models and production systems. Your expertise will transform PyTorch research code into highly optimized, low-latency inference solutions that power our user-facing applications. You'll work closely with our GenAI researchers, vision ML engineers, and backend team to deliver exceptional performance.
- Deploy and integrate researcher-trained model checkpoints into our cloud infrastructure and production pipelines.
- Conduct thorough performance profiling and benchmarking to identify and eliminate computational bottlenecks.
- Implement neural network optimization techniques including quantization, pruning, and architectural refinements while preserving model accuracy.
- Develop efficient training and fine-tuning strategies with optimal precision trade-offs and parallelism.
- Build and maintain scalable multi-GPU inference solutions with sophisticated model parallelism and serving architectures.
- Collaborate with the research team to ensure optimization integrate smoothly with model development workflows.
- Have experience deploying and optimizing deep learning models for production environments, particularly with multi-GPU inference and large-scale model serving.
- Are well-versed in cutting-edge techniques for optimizing both inference and training workloads.
- Possess strong knowledge of efficient attention mechanisms and algorithms.
- Have hands-on experience implementing model quantization and working with inference frameworks.
- Can write production-quality code and successfully integrate ML models into robust inference pipelines.
- Are familiar with various cloud platforms, storage solutions, and modern training frameworks.
- This role is based in San Jose, where we work in person. We believe the best ideas come from being in the same room.
- We sponsor visas. We are committed to working through the process together for the right candidates. If you're currently outside the US, we're also committed to helping you relocate to the US throughout this process.
- We offer generous health, dental, and vision coverage, unlimited PTO, paid parental leave, and relocation support as needed.
- Don't meet every single qualification? That’s okay — we care more about your trajectory than checking every box. If the role excites you and the mission resonates, we'd love to hear from you.
Note: In the event your application is successful and an offer of employment is made to you, any offer of employment will be conditional on the results of a background check, performed by a third party acting on our behalf.
Top Skills
Cloud Platforms
Deep Learning
Model Quantization
Multi-Gpu Inference
PyTorch
Phota Labs San Jose, California, USA Office
San Jose, CA, United States
Similar Jobs
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
As a Senior ML Infrastructure Engineer, you'll design and build scalable platforms for ML inference workflows, collaborating with teams to optimize model serving and enhance system reliability.
Top Skills:
C++GpusPythonRayserveTritonVllm
Artificial Intelligence • Software
The ML Engineer will integrate model architectures, optimize deployment workflows, maintain CI/CD pipelines, and ensure reliability of inference services across large-scale systems.
Top Skills:
CudaFfmpegHuggingfaceKubernetesPythonPyTorchRedisS3-Compatible StorageSglangVllm
Artificial Intelligence • Hardware • Machine Learning • Natural Language Processing • Software • Generative AI
The role involves leading compiler engineering, innovating compiler infrastructure and optimization algorithms for ML performance, and working with various teams to implement solutions.
Top Skills:
MlirPyTorchTensorFlow
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
