Current openings at Phizenix

Machine Learning EngineerMenlo Park, CA On-Site Full-Time/Direct Hire  Client Opportunity | Through Phizenix Phizenix, a certified minority and women-led recruiting firm, is hiring on behalf of an innovative generative AI startup that’s developing diffusion-based large language models—designed for faster generation, multimodal integration, and greater control. We’re looking for a Machine Learning Engineer to join our client’s world-class team. This is a chance to work at the frontier of AI research, turning cutting-edge ideas into high-impact enterprise solutions. Responsibilities Design, train, and optimize state-of-the-art LLMs Build scalable and efficient ML pipelines for training and inference Translate real-world business needs into ML-powered features Collaborate closely with product, research, and engineering teams Support deployment and ensure production-grade reliability Must-Haves PhD in Machine Learning, or related field (or equivalent experience) 2+ years of hands-on experience with machine learning frameworks (e.g., PyTorch) Strong understanding of transformer architectures and LLM concepts (instruction tuning, LoRA, etc.) Experience with distributed training and working in cloud environments (AWS, GCP, or Azure) Proficiency with Git, Docker, and modern ML deployment practices Strong communication skills and ability to collaborate cross-functionally Nice-to-Haves Experience with LLM-serving tools (vLLM, TensorRT, SGLang) Knowledge of MLOps, synthetic data pipelines, or data engineering Familiarity with model optimization techniques (quantization, pruning, etc.) At Phizenix, we value diversity, equity, and inclusion. We’re proud to work with clients who share our commitment to building inclusive teams that reflect a wide range of backgrounds and perspectives. Interested? Reach out to the Phizenix team to learn more and apply. Let’s build the future of AI—together.

ML Infrastructure EngineerMenlo Park, CA | On-Site | Full-Time/Direct Hire   Looking for ML Infra experts (Bay Area preferred) with deep experience in CUDA, GPU optimization, VLLMs, and LLM inference—pure language focus, no vision/audio. Client Opportunity | Through Phizenix Phizenix, a certified minority and women-led recruiting firm, is hiring on behalf of an AI startup pioneering diffusion-based large language models—built for faster generation, multimodal integration, and scalable enterprise deployment. We’re looking for a ML Infrastructure Engineer to help build the infrastructure that powers large-scale model training and real-time inference. You’ll collaborate with world-class researchers and engineers to design high-performance, distributed systems that bring advanced LLMs into production. Responsibilities Design and manage distributed infrastructure for ML training at scale Optimize model serving systems for low-latency inference Build automated pipelines for data processing, model training, and deployment Implement observability tools to monitor performance in production Maximize resource utilization across GPU clusters and cloud environments Translate research requirements into robust, scalable system designs Must-Haves Masters or PhD in Computer Science, Engineering, or a related field (or equivalent experience) Strong foundation in software engineering, systems design, and distributed systems Experience with cloud platforms (AWS, GCP, or Azure) Proficient in Python and at least one systems-level language (C++/Rust/Go) Hands-on experience with Docker, Kubernetes, and CI/CD workflows Familiarity with ML frameworks like PyTorch or TensorFlow from a systems perspective Understanding of GPU programming and high-performance infrastructure Nice-to-Haves Experience with large-scale ML training clusters and GPU orchestration Knowledge of LLM-serving tools (vLLM, TensorRT, ONNX Runtime) Experience with distributed training strategies (e.g., data/model/pipeline parallelism) Familiarity with orchestration tools like Kubeflow or Airflow Background in performance tuning, system profiling, and MLOps best practices At Phizenix, we’re committed to supporting diverse and inclusive teams. This is your chance to shape the systems that power the next generation of AI innovation. Let’s build the future—together.

AI Research Scientist/Engineer Menlo Park, CA | On-Site | Full-Time/Direct Hire    Seeking top-tier PhDs (Bay Area preferred) with ICML/ICLR publications in LLM training and inference optimization—no vision/audio, just pure language; diffusion model experience a plus. Client Opportunity | Through Phizenix (WBENC & Minority-Certified Recruiting Partner) Join a trailblazing AI startup that's reinventing how large language models are built—with diffusion-powered LLMs that generate faster, adapt smarter, and handle multimodal data like no other. We’re looking for a Research Scientist / Engineer who’s ready to move beyond traditional autoregressive methods and help shape the next wave of generative AI. You'll collaborate with pioneers in AI research, design novel model architectures, and scale your ideas from paper to production. What You'll Be Doing Design and refine LLM architectures built on a diffusion-first paradigm Develop cutting-edge training strategies and custom loss functions Translate research into real-world systems for enterprise-scale deployments Explore constraint-aware generation and controlled outputs Push the limits of model efficiency, scalability, and multi-modal capabilities Must-Haves Must have a PhD in Computer Science, Machine Learning, or a related field Hands-on experience with PyTorch and LLM fundamentals (transformers, KV caching, etc.) Should have recent/or any ICLR/ICML publications in LLM inference optimization would be ideal.  Familiarity with diffusion models and distributed model training Solid research-to-production mindset with 2+ years in an ML/AI role Bonus Points For Training LLMs from scratch and optimizing large-scale runs Advanced training tactics (e.g., mixed precision, gradient accumulation) Experience with cross-modal modeling and inference frameworks like vLLM, TensorRT A background in model efficiency, optimization theory, or infrastructure-aware research At Phizenix, we’re proud to partner with diverse and inclusive teams building AI that matters. If you're ready to build, innovate, and publish with one of the boldest teams in AI—this is your moment.