Define how large-scale AI systems for scientific discovery are actually built, trained, and run in production.

This team is building autonomous AI scientists that run full research loops — ingesting large bodies of literature, forming hypotheses, designing experiments, and producing traceable outputs already used across biotech and pharma.

The challenge isn’t just model capability. It’s building the systems that allow these models to be trained, evaluated, and deployed reliably at scale.

You’ll sit at the intersection of model training and systems — owning the infrastructure, pipelines, and experimentation platforms that make long-horizon reasoning systems possible.

This is not research in isolation. It’s building the engine that research runs on.

You’ll work closely with the wider team, translating ambiguous scientific problems into systems that can be trained, iterated on, and deployed in real-world environments.

The company comes from one of the earliest groups working seriously on AI for science, including early language agents and AI-generated biological discoveries. They’re now pushing further with systems capable of reasoning across thousands of papers and large-scale analyses, and moving toward pre-training their own models end-to-end.

The platform is already operating at scale, with tens of thousands of users and millions of queries, and is actively used in scientific workflows today.

What you’ll work on

• Building and scaling training pipelines for large-scale LLM systems
• Developing experimentation platforms that enable fast, reliable iteration
• Designing data pipelines and systems for observability and reproducibility
• Improving how training runs are orchestrated, monitored, and debugged
• Supporting model deployment and inference for complex reasoning systems
• Working closely with researchers to translate ideas into production systems

What they’re looking for

• Experience building and scaling ML systems in production
• Strong background across model training, data pipelines, and deployment
• Experience with large-scale training or distributed systems
• Fluency in frameworks like PyTorch, JAX, or similar
• Strong engineering fundamentals and systems thinking
• Ability to operate across ambiguity and own problems end-to-end

The company

• ~$70M raised, with another round planned
• Platform already at meaningful scale (tens of thousands of users, hundreds of millions of lines of code written by the agent)
• Strong commercial traction 
• Small, high-calibre team working at the intersection of AI and science

📍 San Francisco (on-site or hybrid, remote considered case by case)
💰 $250K–$400K base + equity
Levels: Senior, Staff, Principal
Roles available: ML Engineer, ML Infra, Research Engineers & Research Scientists 

All applicants will receive a response.

Want to build the interface layer for an AI scientist?

You’ll join a team building autonomous AI agents designed to accelerate scientific discovery. The goal is simple, science moves too slowly, and they’re building systems that can change that.

This isn’t a typical frontend role. The product is an integrated research environment where scientists interact directly with AI models, workflows, and generated insights. Your work defines how usable that system actually is.

You’ll sit within the Platform team, working closely with researchers and product to turn complex, often messy scientific workflows into clear, intuitive interfaces.

The challenge is translating depth into clarity without losing fidelity.

You’ll be building high-performance frontend systems where data density, responsiveness, and usability all matter. Real-time interactions, dynamic visualisations, and scalable UI patterns are core to the product.

Your focus will include:

• Building performant React applications for data-heavy workflows
• Designing interfaces for real-time AI interactions and streaming data
• Creating modular, scalable design systems used across the platform
• Translating scientific and model outputs into usable visual interfaces

You’ll need strong frontend fundamentals, but more importantly, the ability to think in systems. Understanding how users navigate complexity, how interfaces guide decision-making, and how performance impacts usability at scale.

There’s a strong emphasis on performance engineering. You’ll be profiling rendering behaviour, optimising asset loading, and ensuring smooth interaction across browsers and devices.

The product itself sits at the intersection of AI, biology, and research tooling. If you’ve worked on complex internal tools, data platforms, or visualisation-heavy applications, this will feel familiar, just at a deeper technical level.

You’ll likely have experience building production frontend systems with React (or similar), working with TypeScript, and handling real-time data flows such as WebSockets or GraphQL subscriptions. Experience with visualisation libraries like D3, Deck.gl or Three.js is highly relevant here.

The environment is highly collaborative. You’ll work closely with researchers to anticipate how the product should evolve, not just respond to specs.

This is an onsite role based in San Francisco, working with a team focused on building something that genuinely pushes forward how science gets done.

Salary: $175,000 – $240,000 + equity
Location: San Francisco, onsite

If you’re interested in shaping how scientists interact with AI systems, apply today.

Want to build systems that actually hold up under long-running AI workloads?

Most agentic systems for science don’t fail at the model layer. They fail because the infrastructure can’t support long-horizon execution.

You’ll join a team building autonomous AI agents that run full research cycles. Ingesting thousands of papers, forming hypotheses, running experiments, and producing traceable outputs used by real scientific teams.

The challenge is making that work in production.

You’ll own the systems behind it. APIs, data pipelines, and platform architecture designed for long-running workloads, large-scale ingestion, and iterative experimentation loops. This is full-stack in scope, but backend in depth, where system design decisions directly impact what the platform can do.

You’ll be working across:

• Backend services in Python or Node, building scalable APIs (FastAPI/REST)
• Data pipelines supporting agent execution and scientific workflows
• Cloud infrastructure (AWS/GCP), containerisation (Docker, Kubernetes)
• CI/CD, observability, and reliability for systems under continuous load

This isn’t a generalist full-stack role. You’ll need to understand how systems behave under heavy data and compute demands, and be comfortable making architectural trade-offs across distributed systems.

The team is small, high-calibre, and already running real workloads with revenue traction. Backed by $70M+, they’re building infrastructure that defines how AI is applied to scientific discovery.

Salary: $200,000–$350,000 + equity
Location: San Francisco (onsite)

Want to build the systems that make AI agents actually work in production?

Most agents fail outside controlled environments, not because the models are weak, but because the systems around them can’t represent how real work happens.

This team is building that missing layer...

Their platform sits inside enterprise workflows, capturing how tasks are executed across tools, then structuring that data so models and agents can actually use it. Real operational context, not synthetic benchmarks.

As a Full Stack Engineer, you’ll focus on the backend and product systems that make this usable in production.

You’ll design workflow data models, build high-throughput pipelines, and ship full-stack features used by real customers. This sits across distributed systems, data engineering, and LLM integrations.

Tech stack includes TypeScript (NestJS, React, Vite, TanStack), PostgreSQL, and AWS/GCP, with OpenAI and Anthropic models integrated into core systems.

You’ll join a small, highly technical, Accel-backed team that’s already post-revenue and scaling with enterprise customers. This isn’t speculative infrastructure, it’s being used.

Experience with Python pipelines, Terraform monorepos, or Rust/Swift is useful, but not essential.

What matters is your ability to build systems that hold up in real-world complexity.

📍 San Francisco (on-site)
 💰 $160K–$280K base + equity + additional comp

If you’re interested in building the layer that makes AI agents usable, this is where that work is happening.

All applicants will receive a response.

• PhD and/or publications at top conferences across long-horizon reasoning, RL, or similar
• Post-training experience (RLHF, DPO, reward modelling)
• Experience working on open-ended research 

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.

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.