ML Research Engineer (Model Training)

Metamorphic•Palo Alto, CA

1d•Remote

About The Position

Metamorphic is developing new approaches to intelligence by combining machine learning with large-scale experimental neuroscience, informed by the principles that make the brain efficient, flexible, and robust. We are building foundation models trained on rich, continuous neural data — a high-resolution model of the brain at a scale never before possible. Our founding team spans machine learning, neuroscience, and neurotechnology, with prior work including the MICrONS project, Neuropixels, and the Enigma project, as well as foundational scientific contributions in learning, neural computation, and generative modeling. Our work sits at the frontier of AI research, and we believe the highest-impact discoveries will come from researchers and engineers working as a single, tightly collaborative team. The name Metamorphic reflects our belief that the next advances in intelligence will come from a change in form, beyond scale — from artificial to natural intelligence. About the Role We are hiring Research Engineers to build end-to-end systems that take data from scientific databases all the way through to trained models, evaluation outputs, and optimized production-ready inference. This means building and maintaining the pipelines, orchestration layers, compute infrastructure, and operational tooling that make large-scale multimodal model development reliable, observable, and fast. The role spans workflow orchestration, GPU compute management, experiment execution, evaluation, model artifact management, inference optimization, and production serving. You will design the systems that coordinate complex ML workflows across heterogeneous infrastructure, support rapid iteration by researchers, and ensure that models move cleanly from experimentation into robust, low-latency, cost-efficient deployment. You'll have substantial autonomy to shape foundational technical decisions on a small, high-impact team. You'll thrive in this role if you: Are excited about building the systems that make frontier ML research possible, reliable, and fast Prefer deeply engineering-focused work and enjoy owning production-quality systems end to end Are comfortable moving across the stack, from orchestration and infrastructure to model runtime and serving interfaces Thrive in fast-paced environments where priorities can shift toward the most important operational or research need Enjoy debugging ambiguous, high-leverage problems that span multiple technical layers Care about building tooling and abstractions that make researchers dramatically more effective Are enthusiastic about working as part of a single, deeply collaborative team pursuing large-scale AI research

Requirements

Bachelor’s degree or higher in Computer Science, Machine Learning, Computational Neuroscience, or a related field
Strong software engineering skills in Python
Deep familiarity with PyTorch and modern machine learning workflows
Experience building and operating production-grade ML systems, platforms, or pipelines that support model development at scale
Experience with workflow orchestration frameworks and designing reliable multi-stage pipelines for complex ML or data systems
Experience with MLOps practices including experiment tracking, artifact management, model versioning, reproducibility, and deployment workflows
Experience with containerization technologies such as Docker and Kubernetes
Experience with distributed compute environments for training, evaluation, and/or inference workloads in research or production settings
Strong debugging skills across multiple layers of the stack
Experience building or optimizing model inference and serving pipelines
Experience building observability, monitoring, and logging systems for ML infrastructure

Nice To Haves

Experience with compute orchestration frameworks (e.g. Kubernetes, Ray, SageMaker, SkyPilot)
Experience with multimodal data pipelines spanning video, time-series, and structured scientific data
Experience with model registries and artifact sharing systems (e.g. HuggingFace Hub, W&B Registry)
Experience with inference optimization techniques (e.g. quantization, distillation, KV-cache optimization)
Background as a systems engineer, platform engineer, or infrastructure engineer supporting ML workloads

Responsibilities

Build and maintain pipelines, orchestration layers, compute infrastructure, and operational tooling for large-scale multimodal model development.
Manage GPU compute, experiment execution, evaluation, model artifact management, inference optimization, and production serving.
Design systems that coordinate complex ML workflows across heterogeneous infrastructure.
Support rapid iteration by researchers.
Ensure models move cleanly from experimentation into robust, low-latency, cost-efficient deployment.
Shape foundational technical decisions on a small, high-impact team.