Are you the kind of engineer who enjoys building complex systems that help models learn, not by training them directly, but by shaping the worlds they inhabit?

This team builds large-scale environments and benchmarks that frontier AI labs use to test and steer their models. Their goal is to make reinforcement learning measurable, creating rich, hyperrealistic simulations where agents can reason, act, and be safely evaluated.

You’ll work at the intersection of software engineering, reinforcement learning, and experimental research, designing the frameworks and pipelines that let agentic AI systems act, learn, and improve through interaction, not static data.

You'll Bring

• Strong Python and software fundamentals who enjoy building ML infrastructure.

• Experience in reinforcement learning, rewards, environment dynamics, evaluation loops.

• Worked with browser/API simulations (Playwright, Selenium) or distributed compute.

• Experience with open-ended problem spaces and a desire to shape the tools driving safe AGI progress.

It’s a technically deep team of ML engineers and researchers from leading labs and tech companies, developing the simulation and evaluation backbone for next-generation agents.

Compensation: $200,000–$250,000 base + equity
Location: San Francisco (on-site, relocation supported)

All applicants will receive a response.

Want to build the simulated worlds that test what frontier models are really capable of?

This is a chance to join a team advancing the science of post-training and scalable evaluation — building reinforcement learning environments that push reasoning, planning, and long-horizon behaviour to their limits.

Instead of static benchmarks, you’ll create dynamic simulations that measure real intelligence — not just accuracy. You’ll design new post-training algorithms (RLHF, DPO, GRPO and beyond), develop richer reward models that move past exact-match scoring, and build evaluation frameworks that define how next-generation AI is trained, aligned, and understood.

The work combines deep research with hands-on implementation — from writing papers to seeing your methods deployed in live systems. It’s ideal for researchers who care about bridging academic insight and practical impact, helping AI progress beyond metrics that no longer tell the whole story.

You’ll bring:

• Research experience in post-training, reinforcement learning, or evaluation for LLMs.

• Strong understanding of transformer models and experimental design.

• Publication record at leading venues (NeurIPS, ICLR, ICML, ACL, EMNLP).

• PhD or equivalent research experience in CS, ML, NLP, or RL.

Package: Up to $300K base (DOE) + meaningful equity + comprehensive benefits (401k, unlimited PTO, relocation and sponsorship available).
Location: On-site in New York (preferred).

If you want to shape how AI is trained, tested, and trusted — this is the place to do it.
All applicants will receive a response.

• Deep research experience in post-training or RL methods (RLHF, DPO, GRPO, reward modelling).
• Strong background in training and evaluating large language models.
• Proven publication record at top-tier venues (NeurIPS, ICLR, ICML, ACL, EMNLP).
• Experience leading research teams and scoping high-impact projects.
• Curiosity, creativity, and the ability to thrive in a fast-moving startup environment.
  
  

Ready to lead the development of a foundation model that powers the next generation of Agentic AI?

This is a hands-on leadership role where you’ll train models from scratch, make the key architectural decisions, and grow your own team. The work will build a Foundation model underpinning AI systems that think, reason, and act in mission-critical environments.

You’ll join a well-backed company founded by an entrepreneur with a previous billion-dollar exit, already partnering with Fortune 100 and 500 clients. Their latest funding round is being channelled directly into compute and team buildout — a substantial commitment to creating truly differentiated AI technology, powered by the foundation model you’ll help design and train.

The challenge: build reasoning-capable foundation model to power Agents, another team in the company will fine-tune and adapt from specific industry use-cases and clients. Your team will work at scale, combining pre-training and post-training approaches while ensuring models meet regulated industry requirements. This is about more than just building models — it’s about creating infrastructure, teams, and frameworks that define the future of AI in high-stakes settings.

Your focus will include leading pre-training and post-training of foundation models, architecting reasoning-capable LLMs, scaling a 15–20 person research and engineering team over 12 months, and integrating models into domain-specific applications.

You should bring:

• Staff/Principal-level experience with hands-on model training.

• Deep expertise in pre-training or post-training (ideally both).

• Track record of driving impactful projects to completion.

• Strong understanding of LLM architectures and large-scale training.

• Experience leading complex technical initiatives across teams.

Nice to have: experience in regulated/safety-critical AI, reasoning or planning architectures, open-source model development, or prior technical leadership.

Package: $350k base (negotiable) + substantial stock, healthcare, 401k, 20 vacation days, and flexible working.

You must be based in the SF Bay Area. US citizens and green card holders only.

This is an opportunity to lead foundation model development with the resources, autonomy, and backing to deliver groundbreaking technology.

All applicants will receive a response.

Build the foundational models that will give AI agents true 3D understanding

Want to solve the fundamental challenge of how AI systems perceive and reason about 3D geometry?

This Lead Applied Scientist role puts you at the forefront of creating perception capabilities for the next generation of agentic AI systems. You'll lead a team building discriminative and generative models introduced into agentic workflows, solving complex challenges in agentic AI for industrial applications.

You'll be joining a well-funded startup developing AI agents for advanced design and manufacturing workflows. Your work will bridge the gap between the physical world and intelligent reasoning systems, creating models that understand CAD data, meshes, and point clouds at a level that enables autonomous decision-making.

This role offers the opportunity to hands on lead a team to build 3D computer vision capabilities from the ground up. You'll be establishing an entirely new domain within the research team, with significant autonomy to define evaluation strategies, model objectives, and technical direction. Your models will form the perception backbone that enables agents to truly understand and manipulate the 3D world.

Your technical challenges:

• Build models that understand diverse 3D data types (CAD, mesh, point cloud) and learn transferable representations across formats
• Handle messy, lossy, or incomplete real-world data - moving beyond clean synthetic geometry to tackle industrial reality
• Scale training across multiple 3D tasks: segmentation, classification, correspondence, and eventually generation
• Create evaluation pipelines that meaningfully assess model performance and enable continuous production monitoring
• Work toward a foundational 3D model supporting both discriminative and generative tasks, integrated into broader agentic AI architecture

Your expertise should include:

• Deep specialisation in 3D computer vision (ideally including a PhD in Computer Vision)
• Strong knowledge of modern 3D architectures (PointNet++, MeshCNN, 3D Gaussian Splatting, diffusion models, VLMs)
• Proven ability training large-scale deep learning models with PyTorch
• Solid applied research skills - can implement novel architectures from papers and make them work in practice
• Experience with multimodal or vision-language model development

Nice to have:

• Background working with CAD data or industrial design workflows
• Experience in complex topics such as robotics, autonomous driving, or AR/VR with 3D perception focus
• Familiarity with SLAM, pose estimation, or differentiable rendering

You'll join a research team that values ownership and rapid iteration, with the resources to pursue ambitious technical goals. The company provides abundant compute resources and the freedom to explore foundational approaches whilst ensuring practical impact.

Package includes:

• Base salary: $300,000
• Performance bonus up to 20%
• Medical, dental, and vision coverage
• 401k with up to 3% company match
• 20+ vacation days

You'll need to be based in SF Bay Area or Miami, with a collaborative team environment that encourages innovation and technical excellence.

You must have valid right to work in the US without sponsorship (US Citizenship or Green Card).

If you're excited about creating the 3D perception capabilities that will power the next generation of intelligent agents, we'd love to hear from you.

All applicants will receive a response.

Build the foundational models that will give AI agents true 3D understanding

Want to solve the fundamental challenge of how AI systems perceive and reason about 3D geometry?

This Applied Scientist role puts you at the forefront of creating perception capabilities for the next generation of agentic AI systems. You'll see your models introduced into agentic workflows, solving complex challenges in agentic AI for industrial applications.

You'll be joining a well-funded startup developing AI agents for advanced design and manufacturing workflows. Your work will bridge the gap between the physical world and intelligent reasoning systems, creating models that understand CAD data, meshes, and point clouds at a level that enables autonomous decision-making.

This role offers the rare opportunity to build 3D computer vision capabilities from the ground up. You'll be establishing an entirely new domain within the research team, with significant autonomy to define evaluation strategies, model objectives, and technical direction. Your models will form the perception backbone that enables agents to truly understand and manipulate the 3D world.

Your technical challenges:

• Build models that understand diverse 3D data types (CAD, mesh, point cloud) and learn transferable representations across formats
• Handle messy, lossy, or incomplete real-world data - moving beyond clean synthetic geometry to tackle industrial reality
• Scale training across multiple 3D tasks: segmentation, classification, correspondence, and eventually generation
• Create evaluation pipelines that meaningfully assess model performance and enable continuous production monitoring
• Work toward a foundational 3D model supporting both discriminative and generative tasks, integrated into broader agentic AI architecture

Your expertise should include:

• Deep specialisation in 3D computer vision (ideally including a PhD in Computer Vision)
• Strong knowledge of modern 3D architectures (PointNet++, MeshCNN, 3D Gaussian Splatting, diffusion models, VLMs)
• Proven ability training large-scale deep learning models with PyTorch
• Solid applied research skills - can implement novel architectures from papers and make them work in practice
• Experience with multimodal or vision-language model development

Nice to have:

• Background working with CAD data or industrial design workflows
• Experience in complex topics such as robotics, autonomous driving, or AR/VR with 3D perception focus
• Familiarity with SLAM, pose estimation, or differentiable rendering

You'll join a research team that values ownership and rapid iteration, with the resources to pursue ambitious technical goals. The company provides abundant compute resources and the freedom to explore foundational approaches whilst ensuring practical impact.

Package includes:

• Base salary: $200,000 (mid/senior) - $250,000 (Staff - negotiable)
• Performance bonus up to 20%
• Medical, dental, and vision coverage
• 401k with up to 3% company match
• 20+ vacation days

You'll need to be based in SF Bay Area or Miami, with a collaborative team environment that encourages innovation and technical excellence.

You must have valid right to work in the US without sponsorship (US Citizenship or Green Card).

If you're excited about creating the 3D perception capabilities that will power the next generation of intelligent agents, we'd love to hear from you. All applicants will receive a response.

Build the ML infrastructure that powers cutting-edge AI across multiple domains

Ready to architect MLOps systems from the ground up for a fast-growing AI team? This greenfield opportunity offers complete autonomy to design and build training pipelines for LLMs, computer vision models, and other deep learning architectures that will power next-generation AI applications.

You'll join a well-funded startup ($20M+ raised, with a new round expected this year) developing production-grade AI solutions across regulated industries including healthcare, aerospace and manufacturing. Founded by a successful entrepreneur with a previous billion-dollar exit, they're already partnering with Fortune 100 and 500 clients where standard AI approaches fall short.

This role offers exceptional technical ownership - you'll build their ML infrastructure from current basic tooling to production-scale systems that support their rapidly expanding applied AI team. They have significant GPU resources with substantial budget growth expected. As the team scales to ~20 people within the year, there's high potential for you to lead future MLOps hires.

The challenge is substantial: creating infrastructure that supports training across multiple modalities - from LLMs to computer vision models. You'll work with large compute resources and have complete autonomy to select and implement the tooling that will define how the team operates for years to come. Your initial focus will be establishing robust training and evaluation pipelines, then scaling to enterprise-grade data workflows with versioning, monitoring, and automated deployment systems.

Your focus:

• Build training and evaluation pipelines for LLMs, vision models, and other deep learning architectures
• Design distributed training systems on multi-GPU clusters across model types
• Create scalable data pipelines, versioning systems, and model checkpointing workflows
• Implement model serving infrastructure with tools like vLLM, Triton, and TorchServe
• Establish comprehensive monitoring, experiment tracking, and reproducibility systems
• Support a rapidly growing applied AI team with robust CI/CD workflows for ML systems

You should have:

• 3+ years building MLOps infrastructure or ML systems in production environments
• Hands-on experience with training pipelines for deep learning models (LLMs, CNNs, transformers)
• Strong expertise with AWS and Kubernetes (mandatory requirements)
• Proficiency with Python, PyTorch/TensorFlow, and distributed training libraries
• Experience with model tracking tools like Weights & Biases or MLflow
• Understanding of modern ML architectures across multiple domains

Nice to have:

• Experience with LLM inference tools (vLLM, SGLang, RayServer)
• Ray experience for distributed computing
• Knowledge of mixed-precision training, quantisation, and model optimisation
• Computer vision workflow experience
• Data versioning tools (DVC, LakeFS)
• Early-stage startup experience

You'll receive:

• Competitive base salary: circa $250K (based on experience)
• Significant stock package in a fast-growing company
• Access to substantial GPU budget with expected growth
• Healthcare (medical, dental, vision) and 401k with matching
• 20 vacation days plus flexible working arrangements

You must be based in SF Bay Area or Miami (relocation is provided to Florida only). At this time we can only consider US citizens or green card holders.nd.

Ready to build the infrastructure that powers the future of production AI? All applicants will receive a response.

Build agentic AI for industries where decisions truly matter - from healthcare to aerospace

Ready to create AI agents that solve real-world challenges in regulated environments? Join a well-funded startup developing secure, explainable AI systems for industries where standard approaches fall short.

You'll work as an AI Engineer in the Applied Science team, taking AI models from research to production and integrating agentic AI systems into real applications. This engineering-focused role involves building and deploying AI automation tools that tackle complex industry workflows across healthcare, aerospace, defence, and manufacturing.

Founded by a successful entrepreneur with previous billion-dollar exits, this company has raised $20M+ and is already partnering with Fortune 500 clients in regulated STEM fields. They're assembling a world-class team to create the next generation of secure, transparent AI agents.

The role offers unique technical challenges in agentic systems for enterprise environments, with access to significant compute resources and the opportunity to shape the direction of a fast-growing team in a no-ego culture focused on building working solutions.

Your focus:

• Develop AI-driven reasoning agents and frameworks for automating complex tasks
• Build and optimise RAG (Retrieval-Augmented Generation) pipelines
• Distill and fine-tune AI models to improve reasoning, automation, and decision-making
• Deploy AI models into scalable, production-ready systems using Python and cloud infrastructure
• Collaborate with researchers to operationalise AI advancements into real applications
• Work with complex datasets (proprietary, customer, and synthetic data)

You should have:

• Proven experience building and deploying AI applications (ideally in a startup or fast-moving environment)
• Hands-on expertise with LLMs and agentic frameworks (AutoGen, CrewAI, LangChain, DSPy, or Haystack)
• Strong background in AI-powered automation and orchestration workflows
• Experience with vector databases and retrieval systems (LlamaIndex, FAISS, Pinecone, Weaviate)
• Solid coding skills in Python and familiarity with cloud deployment tools (AWS, GCP, or Azure)

Nice to have:

• MLOps experience and production ML system deployment
• Experience with fine-tuning AI models

You'll receive:

• Competitive base salary: $160K-$230K (negotiable based on experience)
• Up to 20% performance bonus
• Significant options package
• Healthcare (medical, dental, vision) 
• 401k with up to 3% match 
• 20 vacation days plus 10 sick days with flexible working hours
• Relocation allowance for moves to Florida

You'll be already be based in SF Bay Area or willing to relocate to Miami, Florida, with relocation support provided. The team values clear communication, hands-on building, and collaborative problem-solving in an environment where your work directly impacts the product.

This is an ideal opportunity for an ML Engineer who has built AI applications before and wants to be part of a small, fast-moving team tackling autonomous workflows that solve real problems in mission-critical environments.

Ready to help shape computing intelligence that amplifies human innovation? All applicants will receive a response.