New York City
$200,000–$250,000 + equity

Define how AI agents actually learn in production.

This team is building the foundational learning framework behind enterprise AI systems.

Not prompt wrappers.
Not repeated fine-tuning.

A system that formalises how work gets done and allows agents to improve continuously in real environments.

You’ll design architectures that turn operational behaviour into structured, executable intelligence — making knowledge compound over time through reasoning loops, persistent memory, and human-in-the-loop feedback, without degrading performance.

You’ll work directly with experienced founders and live enterprise customers on problems where reasoning, context, and workflow execution intersect.

What you’ll work on

• Expanding the core learning framework that governs how agents improve

• Designing structured context and memory layers

• Building reasoning loops and feedback systems

• Creating continuous learning pipelines from live operational data

• Shipping production-grade Python systems into real deployments

What you’ll bring

• Experience building non-trivial LLM systems in production

• Designed agentic workflows involving reasoning, memory, and tool use

• Strong Python engineering and systems thinking

• Clear ownership of end-to-end AI systems

The company

• Series A backed by Sequoia ($28M)

• Platform approaching one trillion tokens processed

• Major enterprise customers live

• Small, engineering-led team building the learning layer enterprise AI will depend on

Everyone will receive a response.

Research Engineer – Computer Vision & Machine Learning

Want to build vision systems that let machines understand the physical world as naturally as we do?

This role sits within a highly technical team developing a new class of computing devices where perception, language, and interaction are tightly integrated. Vision is a core capability. Your work will directly influence how machines see, reason about space, and collaborate with humans in real-world environments.

You’ll join a specialist vision group working across 3D computer vision and machine learning. The problems sit at the boundary between learned models and physical reality, including gaze tracking, SLAM, multi-camera geometry, and systems that explicitly model optics, refraction, and light transport. The focus is on geometry-aware, physically grounded approaches rather than purely pixel-driven modelling.

This is a hands-on research engineering role. You’ll move between reading papers, building and training models, designing datasets, running controlled experiments, and deploying onto real hardware. You’ll work closely with firmware and hardware teams to ensure models operate reliably on-device.

Your work will include:

• Developing ML models across 3D perception, tracking, and spatial understanding

• Designing model architectures, training pipelines, evaluation frameworks, and inference systems

• Working with large-scale, multi-camera and sensor-rich datasets

• Translating state-of-the-art research into robust, production-ready systems

• Creating new approaches when existing methods do not meet performance or physical constraints

You’ll have genuine technical ownership. The team values clear thinking, strong experimental discipline, and the ability to make informed bets on promising ideas.

You’ll likely bring end-to-end experience building computer vision and ML models, alongside strong familiarity with modern research in 3D or geometry-aware vision. Hands-on experience with PyTorch or JAX is expected, as is comfort working with complex datasets. The ability to operate independently in ambiguous environments is important, as is clear communication across research, hardware, and product teams.

A Bachelor’s degree or higher in computer science, machine learning, computer vision, applied mathematics, or a related field is required. A Master’s or PhD is a plus, particularly if you’ve worked on geometry-aware or physically informed modelling approaches. Experience deploying ML systems into real products or working in high-ownership startup environments would be valuable.

Compensation: $190,000 - $320,000 base (depending on experience) + equity
Benefits: 401(k) matching, 100% employer-paid health, vision, and dental insurance, unlimited PTO and sick time, medical FSA matching
Location: San Francisco, on-site collaboration required

If you’re motivated by building geometry-aware vision systems that connect AI to the physical world in meaningful ways, we’d like to hear from you!

All applicants will receive a response.

Interested in advancing how agents and LLMs learn from feedback in realistic environments?

You’ll be joining a research-driven AI company building reinforcement learning simulation environments for agents and large language models, with a focus on post-training, evaluation, and scalable supervision. 

Their tools are already used in production by leading AI labs and enterprises and due to demand they are growing fast.

As a Research Scientist, you’ll work hands-on on fundamental problems spanning LLM post-training, RL environments, and agentic evaluation. Your work will shape core methods and benchmarks, and you’ll see your research deployed into production systems. The team actively publishes and collaborates with external research labs, with recent work appearing at ACL and NeurIPS.

What you’ll do

• Conduct research on LLM post-training methods (RLHF, RLAIF, RLVR)

• Design and build realistic RL simulation environments for agents

• Develop agentic evaluation and supervision frameworks

• Create and maintain benchmarks for emerging AI capabilities

• Collaborate with engineers to take research from idea to deployed systems

What you’ll bring

• Experience in applied research in reinforcement learning, LLM post-training, or agent-based systems

• Strong understanding of transformer architectures and LLM fine-tuning

• Ability to translate research ideas into working, production-ready systems

Nice to have

• Publications at top-tier venues (NeurIPS, ICML, ACL, EMNLP)

• Experience working on evaluation, safety, or oversight for advanced AI systems

• Prior work on large-scale training or simulation environments

SF-based. Compensation up to $300k base (flexible, DOE) plus equity, unlimited PTO, and benefits.

Interested in working on the foundations of AI training, evaluation, and safety—while publishing high-quality research that ships?

All applications will receive a response.

Build production systems that bring 3D AI models to life in real-world applications

Ready to bridge cutting-edge 3D computer vision research with robust, scalable production systems? This ML Engineer role focuses on deploying 3D perception models into live agentic workflows where reliability and performance are paramount.

You'll be joining a well-funded startup developing AI agents for advanced design and manufacturing. Your role centres on creating the infrastructure that makes 3D understanding truly practical - from real-time inference pipelines to comprehensive monitoring systems that ensure geometry-aware agents perform reliably in production.

This position offers the opportunity to shape how 3D AI models integrate into agent decision-making pipelines. You'll work closely with applied scientists to productionise breakthrough research whilst building robust systems that handle the unique challenges of geometric data in mission-critical applications.

Your technical focus:

• Architect inference pipelines for 3D vision models handling diverse data types (CAD, mesh, point cloud)
• Build monitoring systems that meaningfully evaluate model performance on real-world, messy geometric data
• Create robust deployment infrastructure scaling across multiple 3D tasks: segmentation, classification, correspondence, and generation
• Implement model lifecycle management supporting both discriminative and generative 3D capabilities
• Design observability frameworks enabling continuous production assessment of 3D model performance

Your background should include:

• 3-10+ years industry experience as an ML Engineer / Computer Vision Engineer
• Proven experience deploying models, especially vision or 3D models
• Strong Python and PyTorch skills with engineering discipline around testing and performance profiling
• Experience with observability tools and ML monitoring best practices
• Deep understanding of challenges specific to deploying 3D models (geometric artifacts, mesh quality, robustness)

Valuable additional experience:

• Working with CAD systems, robotics stacks, or AR/VR environments
• Agent frameworks, planning pipelines, or LLM-integrated systems
• 3D data evaluation methodologies and debugging tools
• Any experience in 3D tools such as WebGL, Three.js, or Blender scripting for 3D visualisation would be useful but not essential.

You'll be establishing the infrastructure foundation for an entirely new capability domain, with high ownership and responsibility for defining production standards and deployment strategies.

Package includes:

• Competitive salary: $180,000-$240,000 
• Performance bonus up to 20%
• Medical, dental, and vision coverage
• 401k with up to 3% company match (after 3 months)
• 20 vacation days, 10 sick days, and flexible working arrangements

Based in SF Bay Area or Miami, working alongside a research team that values practical impact and technical excellence.

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

If building the systems that make breakthrough 3D AI research truly useful appeals to you, we'd love to discuss this opportunity. All applicants will receive a response.