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Physical Superintelligence

60 Physical Superintelligence Jobs Hiring Near You

Lambda

Senior HPC Platform Hardware Engineer

San Jose, CA · On-site

$129K - $172K/yr

Hardware Engineering at Lambda is responsible for building and scaling the physical infrastructure behind the Superintelligence Cloud. Our scope spans the full hardware lifecycle: roadmap and ...

Lambda

Staff Connectivity Engineer

San Jose, CA · On-site

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of ... As a Staff Connectivity Infrastructure Engineer, you will drive and support the Physical ...

Lila Sciences

Co-op, Mechanical Engineer

Cambridge, MA · On-site

... physical science applications. From CAD to prototype to initial test builds, your work will directly contribute to Lila's mission of building Scientific Superintelligence. What You'll Be Building

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Physical Superintelligence Jobs Information

Infographic showing various job openings at Physical Superintelligence in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 44% Physical, and 56% Remote job distribution.

Senior Machine Learning Engineer I, Physical Sciences

Lila Sciences

Cambridge, MA

$133K - $176K/yr

Full-time

Re-posted 15 days ago

Job description

Your Impact at LILA

This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity scientific use cases in materials, chemistry and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today's greatest challenges in physical sciences.

What You'll Be Building

  • Design, implement, and maintain endtoend ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
  • Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
  • Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
  • Contribute to technical design reviews, coding standards, and mentoring of best practices.

What You'll Need to Succeed

  • BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
  • Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
  • Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
  • Handson experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
  • Clear communication and collaboration in crossfunctional settings.

Bonus Points For

  • Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
  • GPU optimization experience (CUDA, Triton, compilation, distributed training).
  • Prior contributions to opensource ML or scientific software.
  • Experience with workflow orchestration, data provenance, or largescale compute environments.

Apply