HEY,
BOSTON

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.

Senior Data Scientist – Generative AI About the Company A fast-growing technology firm is transforming how the global insurance market operates by automating complex workflows across sales, servicing, and claims. Starting with cutting-edge voice automation and now expanding into full end-to-end workflow automation, the team is pushing the boundaries of reasoning agents capable of managing the entire spectrum of insurance operations. Location Boston, MA or Berkeley, CA – hybrid schedule (2 days per week in-office) The Role We are seeking an experienced Data Scientist to drive large-scale Generative AI initiatives. You will design and build advanced LLM-powered conversational pipelines and automation systems that reshape how insurance tasks are performed. This is a hands-on, strategic role for someone who can both set a high-level vision and dive into the technical details. - Key ResponsibilitiesDesign, architect, and build GenAI conversation pipelines across chat, voice and SMS using techniques such as multi-agent orchestration and retrieval-augmented generation (RAG).Develop scalable evaluation pipelines to measure the performance of enterprise-grade AI/ML solutions.Work closely with ML engineers to deploy, operate and continually optimize large-scale solutions.Collaborate with product managers to shape user journeys, design feedback loops, and analyse user telemetry.Deliver end-to-end AI/ML product experiences tailored to insurance workflows.- What We’re Looking For5+ years of industry experience delivering ML/AI solutions in production.Proven success in building and scaling GenAI or Agentic AI systems in a professional setting.Ability to think strategically while remaining hands-on with optimisation and implementation.Comfort working in a fast-moving, ambiguous environment and translating complexity into clear action.Strong communication skills for sharing innovations internally and externally.Deep understanding of machine learning algorithms and evaluation frameworks, including:Deep learning frameworksSupervised fine-tuning of LLMsPreference optimisation methods for domain adaptation in LLMsTrack record of applying trustworthy AI/ML practices in collaboration with cross-functional stakeholders.- Compensation and BenefitsCompetitive base salary (range dependent on experience)Meaningful equity participationComprehensive benefits package, including location-specific plan options

AI Evaluation Engineer $160,000 - $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

Senior Deep Learning Scientist $160,000 - $250,000 (DOE) Onsite – New Haven, Connecticut A cutting-edge biotech startup is seeking a Senior Deep Learning Scientist to join their team. This innovative company is pioneering a first-of-its-kind platform to conduct preclinical studies, aiming to revolutionize the understanding and treatment of neurological diseases.As a Senior Deep Learning Scientist, you will play a pivotal role in designing and implementing AI models that integrate complex biological signals. You'll be at the forefront of pioneering work in areas such as generative graph representation learning, contributing to the development of novel AI architectures tailored to the intricacies of human brain biology.Key Responsibilities:Design, develop, and deploy state-of-the-art deep learning models for analyzing multi-modal biological data.Develop deep learning architectures incorporating biological inductive biases, and explore generative graph representation learning to uncover novel patterns in brain data.Work closely with bioinformatics, experimental biology, and engineering teams to integrate multi-modal datasets into cohesive AI frameworks.Optimize deep learning pipelines for petabyte-scale datasets and ensure models are scalable on high-performance computing infrastructures.Publish research findings and present at scientific conferences to contribute to the broader AI and biomedical communities. Requirements:PhD or Post-doc in Computer Science, Machine Learning, or a related STEM field with a strong demonstrated track record of applying deep learning to biological problems.The ability to translate conceptual research frameworks into deployable architectures.Comfort working across research and applied implementation.Proven experience with GNNs. Experience with generative graph representation learning is a significant plus.Expertise in PyTorch with the ability to build and deploy scalable models.Familiarity with developing production-quality pipelines, cloud computing, and model deployment best practices.Demonstrated ability to research and implement novel deep learning architectures tailored to complex STEM datasets.Experience with high-performance computing (HPC) environments or distributed training techniques for large-scale GNN models. Apply now or reach out to Ben at benjamin@deeprec.ai to learn more!

Job Title: AI Researcher Location: Bay Area (Hybrid) Compensation: $250K–$290K + Equity We’re hiring on behalf of a fast-growing, well-funded Bay Area startup redefining video creation using generative AI. Their platform enables video-to-video generation — combining diffusion models, Gaussian splatting, and human-centric 3D rendering. In this role, you’ll:Research and deploy models for video generation, in-painting & 3D simulationWork on human-centric modeling (pose, movement, reenactment)Build on top of diffusion models, transformers, and neural rendering methodsCollaborate with a multidisciplinary team of researchers, engineers & artistsIdeal candidates have:A PhD (or equivalent) in AI, CV, or graphicsPublications in top venues (CVPR, NeurIPS, ICCV, SIGGRAPH, etc.)Strong experience with generative models or visual computingApply now to work on bleeding-edge visual AI with real-world impact.

Job Title: MLOps EngineerWork Arrangement: RemoteLocation: Toronto, CanadaSalary: Up-to $125,000 CADMLOps Engineer – Real-Time AI SystemsWe're looking for an experienced MLOps Engineer to help deploy and scale cutting-edge ML models for real-time video and audio applications. You'll work alongside data scientists and engineers to build fast, reliable, and automated ML infrastructure.Key ResponsibilitiesBuild and manage ML pipelines for training, validation, and inference.Automate deployment of deep learning and generative AI models.Ensure model versioning, rollback, and reproducibility.Deploy models on AWS, GCP, or Azure using Docker and Kubernetes.Optimize real-time inference using TensorRT, ONNX Runtime, or PyTorch.Use GPUs, distributed systems, and parallel computing for performance.Create CI/CD workflows (GitHub Actions, Jenkins, ArgoCD) for ML.Automate model retraining, validation, and monitoring.Address data drift, latency, and compliance concerns.What You Bring3+ years in MLOps, DevOps, or model deployment roles.Strong Python and experience with ML frameworks (PyTorch, TensorFlow, ONNX).Proficiency with cloud platforms, Docker, and Kubernetes.Experience with ML tools like MLflow, Airflow, Kubeflow, or Argo.Knowledge of GPU acceleration (CUDA, TensorRT, DeepStream).Understanding of scalable, low-latency ML infrastructure.Nice to HaveExperience with Ray, Spark, or edge AI tools (Triton, TFLite, CoreML).Basic networking knowledge or CUDA programming skills.