HEY,
BOSTON

AI Evaluation Engineer$180,000 Remote (US-based)Are you passionate about shaping how AI is deployed safely, reliably, and at scale? This is a rare opportunity to join a mission-driven tech company as their first AI Evaluation Engineer, a foundational role where you’ll design, build, and own the evaluation systems that safeguard every AI-powered feature before it reaches the real world.This organization builds AI-enabled products that directly helps governments, nonprofits, and agencies deliver financial support to people who need it most. As AI capabilities race forward, ensuring these systems are safe, accurate, and resilient is critical. That’s where you come in.You won’t just be testing models, you’ll be creating the frameworks, pipelines, and guardrails that make advanced LLM features safe to ship. You’ll collaborate with engineers, PMs, and AI safety experts to stress test boundaries, uncover weaknesses, and design scalable evaluation systems that protect end users while enabling rapid innovation. What You’ll DoOwn the evaluation stack – design frameworks that define “good,” “risky,” and “catastrophic” outputs.Automate at scale – build data pipelines, LLM judges, and integrate with CI to block unsafe releases.Stress testing – red team AI systems with challenge prompts to expose brittleness, bias, or jailbreaks.Track and monitor – establish model/prompt versioning, build observability, and create incident response playbooks.Empower others – deliver tooling, APIs, and dashboards that put eval into every engineer’s workflow. Requirements:Strong software engineering background (TypeScript a plus)Deep experience with OpenAI API or similar LLM ecosystemsPractical knowledge of prompting, function calling, and eval techniques (e.g. LLM grading, moderation APIs)Familiarity with statistical analysis and validating data quality/performanceBonus: experience with observability, monitoring, or data science tooling

Distributed Systems EngineerSan Francisco, CA (Onsite)About the CompanyA fast-moving AI research group is building the core video data infrastructure used by leading AI labs and major tech companies. The team is small at around fifteen people, nearly all engineers, and recently pivoted to focus exclusively on high-quality video data at massive scale. The shift has driven significant revenue growth, and they are now planning to expand the team steadily over the next few months.The culture is straightforward: engineering led, product focused, low ego, and built around people who enjoy ownership. They work in person five days a week in their San Francisco office, moving quickly, solving hard problems, and avoiding micromanagement.The RoleThis position focuses on designing and scaling distributed systems that support huge ML and ETL workloads across petabytes of video. You will own core infrastructure: compute scheduling, orchestration, throughput, reliability, cost efficiency, and the internal tooling that keeps the entire engineering group moving at pace.The company is beginning to scale its infrastructure footprint aggressively, and this role will become central to that growth. It is a hands-on IC position suited to someone who has operated critical systems before and wants to shape the foundation of a rapidly expanding platform.What You’ll Work On• Architect and scale distributed systems for large-scale ML and ETL workloads• Build compute orchestration and scheduling across thousands of GPUs• Improve uptime, resilience, and execution speed of high-volume data pipelines• Design pipelines capable of handling petabyte-level video datasets• Lead the development of CI/CD and internal tooling for fast iteration• Partner closely with research engineers delivering new video models and algorithms• Operate in a high-trust environment with strong autonomy and clear ownershipRequirements• 3+ years building foundational distributed systems or data infrastructure• Experience running critical systems at significant scale• Proficient across cloud architectures• Strong coding experience with Go (preferred) and Python• Background building or maintaining large-scale pipelines• Experience with ML-focused CI/CD and automation• Video domain experience is not required• Operates as a strong IC who leads through action• Fully onsite in San Francisco, Monday to FridayCulture Fit• Enjoys ambiguity, problem discovery, and self-direction• Communicates clearly and concisely• Shows strong intellectual curiosity• Low ego, collaborative mindset• Motivated by building core systems in a small, high-caliber teamRed flags include weak communication, low curiosity, or unclear motivation for the domain.Interview ProcessIntro call focused on culture, curiosity, and communicationTechnical discussion on background and complexity of past workProblem-solving session with a research engineerOnsite research problem and collaboration exercise

Senior Computational Materials ScientistAbout the CompanyA global energy-technology organization developing next-generation Li-Metal batteries for electric mobility across automotive, aviation, and advanced energy applications. This team integrates modern machine learning directly into materials R&D, cell design, manufacturing workflows and safety analytics, operating across major hubs in North America and Asia.About the Advanced Computation DivisionThis group serves as the company’s core AI and computational science unit. It brings together computational materials scientists, software engineers and machine learning researchers working hand-in-hand with experimental chemists and product engineers. The team builds intelligent scientific tooling, accelerates materials discovery and supports fast iterative R&D.About the Molecular Discovery PlatformThe company’s flagship platform for AI-accelerated materials discovery analyzes more than 10^8 small molecules across quantum-level, ML-derived and experimentally curated properties. Leveraging GPU-accelerated simulation, large-scale automation and advanced visualization, it enables rapid navigation across vast chemical space.About the RoleThe team is seeking a Senior Computational Materials Scientist to contribute to the development of this platform while advancing simulation capabilities for electrolyte systems, solid electrolyte interphase (SEI) modeling, reaction network methods, force field development and large-scale molecular dynamics acceleration on modern HPC infrastructure.You will collaborate across computation, software, AI and experimental groups to develop tools that connect quantum chemistry, statistical mechanics and machine learning for practical molecular design.Key ResponsibilitiesDesign and execute large-scale quantum chemistry and molecular dynamics simulations using industry-standard tools (e.g., GPU4PySCF, GROMACS, LAMMPS, Gaussian).Develop and refine force fields and interatomic potentials for electrolyte-relevant chemistries.Build and improve simulation workflows for SEI formation, including reaction network analysis and atomistic modeling.Contribute to property-calculation workflows covering key quantum descriptors (HOMO, LUMO, ESP), thermodynamics and kinetics.Automate high-throughput simulation pipelines using Python, HPC schedulers (e.g., SLURM) and distributed compute environments.Integrate new simulation capabilities into the broader molecular discovery platform through APIs or modular Python packages.QualificationsRequiredPhD in Materials Science, Chemistry, Chemical Engineering, Physics or a closely related discipline5+ years of post-PhD experience in computational chemistry or computational materials scienceHands-on experience with major molecular simulation packages (GROMACS, LAMMPS, Gaussian, VASP, Quantum Espresso, ADF, GPU4PySCF or similar)Strong Python skills, including scientific libraries (NumPy, ASE, PySCF etc.) and experience writing reproducible research-grade codeExperience with high-throughput computation and large-scale data workflows on HPC or GPU clustersStrong communication skills and comfort working across experimental, computational and AI teamsPreferredExperience with battery materials, electrolyte systems or solid/liquid interface modelingBackground in force-field development, reactive MD (Polarizable FF, ReaxFF, MLFF) or coarse-grained simulationFamiliarity with cheminformatics concepts (molecular representations, fingerprints, exploration of chemical space)Contributions to open-source simulation frameworks or published methodology papersExperience with unsupervised learning methods (dimensionality reduction, clustering beyond k-means)Exposure to CUDA or GPU-accelerated codingWho Thrives HereYou enjoy working at the intersection of chemistry, physics, ML and large-scale computationYou’re comfortable challenging the limits of standard computational toolsYou have a natural curiosity for molecular behavior, electrolyte chemistry and computational designYou prototype quickly, iterate thoughtfully and value reproducible scientific workflowsYou like building tools that turn raw simulation output into interactive, research-ready platforms

Product Engineer – AI-Driven Materials & Battery Simulation Platform About the CompanyA leading energy-technology firm advancing next-generation battery materials and intelligent energy systems. The team is at the forefront of applying modern machine learning to materials discovery, molecular simulation, and high-performance battery development. Their AI-enhanced Li-Metal and Li-ion platforms are among the first to incorporate electrolyte materials discovered through data-driven scientific methods, enabling progress across mobility, energy storage, robotics and aerospace. What You Can ExpectStrong compensation and benefits, including meaningful equity in a fast-scaling public company.The chance to contribute to an ambitious scientific mission focused on accelerating the transition to cleaner global energy systems.A collaborative workplace where AI, computational science and advanced battery R&D converge.Significant career growth opportunities working alongside top researchers, engineers and domain experts.Role OverviewThe company is seeking a Product Engineer to design and lead an AI-driven molecular simulation and materials informatics platform supporting the development of next-generation battery materials.You will connect advanced AI model architectures with computational chemistry, molecular dynamics (MD) and phase-field simulation. This role centers on building and scaling the scientific computing stack that powers materials discovery and battery R&D across the organization.You will take early-stage AI4Science capabilities — from ML force fields and surrogate models to automated MD pipelines — and turn them into reliable, developer-friendly APIs and internal platforms. Key ResponsibilitiesPlatform and ArchitectureLead the full architecture and delivery of a scientific computing platform that unifies AI models, simulation tools and experimental data.Build and optimize high-performance simulation services in C++ for large-scale MD, phase-field and related materials models.Define and evolve platform interfaces and APIs that expose simulation, data and ML services to internal users.AI-Driven Simulation and AutomationDevelop and operationalize AI/ML models for materials informatics, including ML force fields, surrogate modeling and uncertainty-aware pipelines.Build scalable MD automation systems that manage large batches of simulations, including scheduling, monitoring and data capture.Convert cutting-edge research prototypes into production-grade simulation and AI services.Battery R&D IntegrationCollaborate closely with scientists and experimental teams to translate R&D requirements into practical platform features.Develop simulation tools supporting analysis of dendrite behavior, degradation pathways and electrolyte/material performance.Ensure seamless integration between simulations, experimental workflows and analytics systems.Core CompetenciesExpertise in C++ and scientific/high-performance computingExperience with HPC environments and parallel computing (MPI, CUDA, GPU acceleration, or similar)Strong knowledge of MD simulations and associated toolingAPI engineering and scalable software/platform architectureUnderstanding of battery materials informatics and AI4Science workflowsExperience building automated MD workflows and simulation pipelinesHybrid background across scientific computing and modern software engineeringMinimum QualificationsPhD in Materials Science, Computational Physics, Computational Chemistry or a similar field.At least 1 year of post-graduate experience in computational materials science, including MD or phase-field simulation.Proven ability to build production-grade scientific software in C++ or related systems languages, ideally in HPC environments.Hands-on exposure to AI/ML for materials modeling (ML force fields, surrogate models, automated ML workflows).Experience developing APIs, services and platforms for use by engineering or scientific teams.Strong grounding in algorithms related to materials behaviour (dendrite formation, transport, microstructure evolution).Demonstrated ability to work directly with experimentalists and domain scientists.Preferred QualificationsExperience developing or scaling AI4Science platforms unifying simulation, ML and laboratory/experimental data.Background with cloud-native scientific computing (Kubernetes, containers, workflow engines).Prior exposure to battery R&D (Li-metal, Li-ion, electrolytes, interfaces) and multiscale modeling.Experience leading product or platform engineering initiatives within deep-tech or research-heavy environments.Familiarity with modern data/ML stacks such as Python, PyTorch/JAX/TensorFlow, model registries and workflow orchestration tooling.

Agentic AI Architect / ML Scientist Location: Boston Massachusetts Type: Full time I am representing a deep tech organisation building an AI powered materials discovery platform. Their system integrates advanced model architecture with computational chemistry and large scale simulation. The company has strong financial backing, over 200 million USD in liquidity, and partnerships with leading automotive and energy companies, giving real-world impact to its technology.About the platform At the heart of the company’s work is a high-dimensional system that maps and predicts molecular behaviour at scale. It combines physics-informed models, learned representations, and automated simulations to accelerate discovery of next-generation battery and electrolyte materials. This system also supports agentic reasoning, interpretable predictions, and causal insights, allowing the platform to guide scientific exploration autonomously.What you will work onDesign and develop foundation models and agentic AI systems for scientific applicationsApply multimodal reasoning and causal inference to molecular and materials datasetsBuild interpretable models with next-generation explainability tools for scientific MLDevelop representation engineering approaches to improve model generalisation and accuracyWork closely with chemists, physicists, and engineers to translate scientific challenges into ML solutionsContribute to experiments that validate and benchmark AI predictions in real-world materials discoveryMentor and collaborate with team members across levels to share best practices in AI researchWhat you bringStrong background in ML, foundation model design, or AI research applied to scientific domainsExperience in molecular simulation, materials science, or physics-informed MLKnowledge of agentic AI, causal reasoning, multimodal reasoning, or interpretable ML techniquesFamiliarity with large datasets in chemistry, materials, or battery sciencesAbility to communicate complex AI concepts to scientific and technical teamsResearch mindset with curiosity for building models that can reason and act autonomously

Member of Technical Staff - Pre-Training (Remote US)This is an opportunity to join one of the smartest, most ambitious teams in the AI space. Founded in 2023, this fast-growing research and product company is already being talked about alongside some of the biggest names in foundational model development. They’re building powerful, intelligent agent systems and frontier-scale models - and they believe software engineering is the most direct path toward achieving AGI.With major backing from industry leaders, significant compute infrastructure, and a focus on mission-critical enterprise and public-sector environments, they’re tackling some of the hardest AI challenges out there.The RoleAs a Member of Technical Staff (Pre-Training / Data), you’ll be part of a high-performing Data team inside the Applied Research machinery that powers the company’s pre-training and reinforcement learning breakthroughs. Your goal: build the datasets that make better models possible. This is a hands-on, deeply technical role at the intersection of data engineering, research, and large-scale systems.What You’ll DoBuild, scale, and refine huge datasets made up of natural language and source code to train next-gen language modelsWork closely with pre-training, RL, and infrastructure teams to validate your work through fast feedback loopsStay ahead of the curve on data generation, curation, and pre-training strategiesDevelop systems to ingest, filter, and structure billions of tokens across diverse sourcesDesign controlled experiments that help uncover what works and what doesn’tBe a core voice in shaping how the team approaches data for model training - a vital part of their long-term AGI missionWhat You BringSolid hands-on experience with large language models or large-scale ML systemsStrong track record building or working with massive datasets - from raw extraction through to filtering and packagingExposure to training models from scratch - ideally using distributed GPU clustersProficient in Python and ML frameworks like PyTorch or JAX, plus confidence working in Linux, Git, Docker, and cloud/HPC environmentsGreat if you also have some C++/CUDA, Triton kernels, or GPU debugging backgroundYou’re a thinker and a builder - someone who can read the latest paper and turn it into something real, quicklyWhat’s In It for YouFully remote US37 days of paid time off annuallyComprehensive health cover for you and your dependentsMonthly team meetups - travel, accommodation, and even family attendance coveredHome office and wellbeing budgetA competitive salary plus meaningful equityThe chance to work with some of the brightest minds in AGI and do genuinely original workWhat the Process Looks LikeRecruiter intro callFirst technical interview focused on LLMs, performance, or core engineering skillsSecond technical deep dive into your domain (pre-training, data, scaling, etc.)Culture conversation with the founding engineersFinal discussion on compensation and alignmentIf you’re driven by building systems that could reshape how intelligence works - and you want to be surrounded by people who share that fire - this team is where you belong.

Member of Technical Staff – Frontend (React.js, Next.js)Location: Toronto, Canada (Hybrid)Type: Full-time, Permanent OverviewOur client (Series A, GenAI Content Platform) is hiring a core frontend engineer in Toronto to architect and scale their browser-based animation and video generation interface. You’ll own the React.js / Next.js web app powering AI-driven content creation for a fast-growing global user base. ResponsibilitiesLead frontend feature development using React.js and Next.js (SSR, ISR, SSG).Implement state management patterns (Zustand, Redux, Jotai, etc.).Integrate with REST/GraphQL APIs and real-time ML-driven backend endpoints.Optimise bundle size, rendering, hydration, and caching across devices and network profiles.Build robust testing pipelines (Jest, React Testing Library, Cypress / Playwright).Establish observability for UI performance, error tracking, and release health.Refactor and modularise code for scaling and improved developer experience.Collaborate closely with backend and ML teams on product UX and performance. Requirements5+ years’ professional frontend experience.Expert-level skills in React.js, Next.js, TypeScript, and modern web standards (ES6+, CSS-in-JS, etc.).Track record building and deploying production-grade, customer-facing applications.Strong grasp of rendering lifecycles, VDOM internals, hydration, and frontend performance tuning.Familiarity with edge compute and deployment (Vercel, Cloudflare Workers) and caching (SWR, ISR, CDNs).Bonus: experience with browser media pipelines (Canvas, WebGL, streaming, WebCodecs).Previous start-up or 0-1 product engineering experience preferred.

Member of Technical Staff - Machine Learning Infrastructure/High Performance Inference EngineI’m working with a well-funded AI research company building the technical foundations for a new class of embodied agents and digital humans - systems designed with genuine, human-like qualities that can interact, collaborate, and form real connections with people. Their long-term aim is to scale this work into multi-agent simulations and entire societies of autonomous AI entities.As their Member of Technical Staff (ML Infrastructure), you’d design and scale the platforms that make this possible - from high-performance inference engines to distributed training pipelines and large-scale compute clusters that power intelligent, interactive AI systems. You’d work closely with researchers and product engineers to push the limits of inference performance, strengthen the foundations for agentic AI, and evolve the next generation of training and post-training pipelines.Responsibilities:Accelerate research velocity by enabling SOTA experimentation from day one.Build and optimize the full model training pipeline, including data collection, data loading, SFT, and RL.Design and optimize a high-performance inference platform leveraging both open-source and proprietary engines.Develop and scale technologies for large-scale cluster scheduling, distributed training, and high-performance AI networking.Drive engineering excellence across observability, reliability, and infrastructure performance.Partner with research and product teams to turn cutting-edge ideas into robust, production-ready systems.Qualifications:Expertise in one or more of: inference engines, GPU optimization, cluster scheduling, or cloud-native infrastructure.Proficiency with modern ML frameworks such as PyTorch, vLLM, Verl, or similar.Experience building scalable, high-performance systems used in production.Start-up mindset - adaptable, fast-moving, and high-ownership.Why This Opportunity Stands Out:Elite founding team: Engineers and researchers from MIT, Stanford, Google X, Citadel, and top AI labs.Strong funding and backing: Over $40M raised from Prosus, First Spark Ventures, Patron, and notable investors including Patrick Collison and Eric Schmidt.Serious traction: Their flagship AI companion product has already achieved significant user growth and is generating real revenue.Impact and autonomy: A flat, fast-moving environment where you’ll own critical systems and ship meaningful work within weeks.Longevity in vision: This company is not chasing quick exits - they’re deliberately building what they believe will be a historical company, with long-lasting influence on how humans and AI interact.