HEY,
BOSTON

Senior MLOps Engineer – GPU Infrastructure & Inference Our client is building AI-native systems at the intersection of machine learning, scientific computing, and materials innovation, applying large-scale ML to solve complex, real-world problems with global impact. They are seeking a Senior MLOps Engineer to own and operate a production-grade GPU platform supporting large-scale model training and low-latency inference for computational chemistry and LLM workloads serving thousands of users. This role holds end-to-end responsibility for the ML platform, spanning Kubernetes-based GPU orchestration, cloud infrastructure and Infrastructure-as-Code, ML pipelines, CI/CD, observability, reliability, and disaster recovery. You will design and operate hardened, multi-tenant ML systems on AWS, build and optimize high-performance inference stacks using vLLM and TensorRT-based runtimes, and drive measurable improvements in latency, throughput, and GPU utilization through batching, caching, quantization, and kernel-level optimizations. You will also establish SLO-driven operational standards, robust monitoring and alerting, on-call readiness, and repeatable release and rollback workflows. The position requires deep hands-on experience running GPU workloads on Kubernetes, including scheduling, autoscaling, multi-tenancy, and debugging GPU runtime issues, alongside strong Terraform and cloud-native fundamentals. You will work closely with research scientists and product teams to reliably productionize models, support distributed training and inference across multi-node GPU clusters, and ensure high-throughput data pipelines for large scientific datasets. Ideal candidates bring 5 years of experience in MLOps, platform, or infrastructure engineering, strong proficiency in Python and modern DevOps practices, and a proven track record of operating scalable, high-performance ML systems in production. Experience supporting scientific, computational chemistry, or other physics-based workloads is highly desirable, as is prior exposure to large-scale LLM serving, distributed training frameworks, and regulated production environments.

ML Scientist in AI Explainability  Location: Boston Massachusetts Type: Full time Machine Learning Scientist, AI Explainability and Scientific Discovery We are working with a publicly listed deep tech company operating at the intersection of machine learning, material science, and next generation battery technology. The team is applying AI directly to scientific discovery, with real world impact across energy storage, transportation, robotics, and aerospace. This role sits within an advanced AI research group focused on Large Language Models, AI agents, and explainability in scientific problem solving. Your work will directly influence how new battery materials are discovered and validated using AI. The position can be fully remote. What you will work on You will lead research into machine learning methods for scientific discovery, with a strong focus on multimodal Large Language Models and agent based systems.You will study how LLMs reason, plan, and generate solutions when applied to core scientific and engineering questions, particularly in battery and material design.You will design and optimize training pipelines for large models, tackling challenges around data quality, architecture, scalability, and compute efficiency.You will integrate domain specific data sources such as scientific literature and internal research documents into model training and inference.Your research will be deployed into a production multi agent AI system used for real battery technology discovery.You will collaborate closely with researchers, engineers, and external academic labs, and contribute to publications and conference presentations. What we are looking for An MSc or PhD in Computer Science, Statistics, Computational Neuroscience, Cognitive Science, or a related field, or equivalent industry experience.Strong grounding in machine learning, deep learning, and Large Language Models, with hands on research experience.Solid Python skills and experience with frameworks such as PyTorch or TensorFlow.Experience working with causal graphs and explainability focused AI methods.A proven research track record, ideally including peer reviewed publications.The ability to explain complex technical ideas clearly to both technical and non technical stakeholders.Nice to have Exposure to AI applied to material science, chemistry, or battery systems.Familiarity with recent research methods in LLM optimization and reinforcement learning approaches such as GRPO. What is on offerA highly competitive salary and benefits package, including equity in a publicly listed company.The chance to work on AI for science problems with visible global impact.A collaborative research environment alongside experienced ML scientists, engineers, and domain experts.Strong support for professional development, publishing, and long term career growth.

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

Senior LLM Research ScientistA frontier-stage research group is building a new class of AI systems designed to reason, plan, and act across the physical world. Their mission is to create intelligent agents capable of experimenting, engineering, and constructing in ways that dramatically accelerate scientific and industrial progress. This team combines deep technical pedigree with real-world wins at scale, including major government-funded initiatives. They operate where advanced model research meets robotics, simulation, and automated engineering systems, offering the kind of impact only possible when first-principles science meets ambitious execution. Joining means stepping into a high-ownership environment where you shape core capabilities end-to-end, influence the direction of physical-world intelligence, and help build technology the world has never seen before. Why This Role Is CompellingWork on cutting-edge reasoning, planning, and tool-use models that directly control autonomous engineering systems.Push the limits of SFT, RLHF, DPO, verifier-guided RL, and long-horizon planning in a setting where your research immediately translates into real-world capability.Operate in a high-velocity research culture with exceptional peers across agent systems, simulation, data, and complex toolchains.Have outsized ownership in a small team tackling one of the most ambitious technical problems of this decade.Role Overview The team is looking for an LLM Research Scientist to pioneer next-generation reasoning and agent architectures. Your work will span model design, alignment strategies, structured tool orchestration, and experimentation with agents interacting across real engineering workflows. This position blends deep research with hands-on systems integration, offering both autonomy and scope to lead foundational progress. Key ResponsibilitiesDevelop advanced models and prompting systems for planning, multi-step reasoning, and structured tool use.Lead training initiatives across SFT, RLHF/DPO, verifier-guided RL, and modular expert architectures to strengthen robustness and controllability.Define schemas, tool-calling strategies, policy constraints, safety mechanisms, and recovery pathways for agent behavior.Partner closely with engineering, simulation, and data teams to test, train, and evaluate models embedded in real production-like toolchains.QualificationsSignificant experience in LLM research, agent reasoning models, or structured tool-use frameworks.Strong background working with SFT, RLHF, DPO, or reinforcement-learning-from-verification methods.Demonstrated ability to design, analyze, and improve long-horizon behaviors and decomposition strategies.Comfortable working across ML research, systems engineering, and real-world experimentation in a fast-moving environment.A track record of excellence and ownership in technically demanding domains.

Senior RL Research Scientist / Reinforcement Learning ScientistJoin a frontier AI team building systems that can act in the physical world, experimenting, optimizing, and controlling real processes through advanced ML, simulation, and automation. This group is pushing the boundaries of physical intelligence, backed by significant long-term funding and a mandate to invent from first principles. If you want to:Work on problems few teams in the world can touchBuild RL systems that power real tools, workflows, and scientific processesOperate in a fast, high-ownership, deeply technical culture…this is the kind of role that defines a career. The Role You’ll design and deploy reinforcement learning systems that control complex tools, optimize multi-step processes, and operate across high-fidelity simulations and digital twins. Expect hands-on research, real-world experimentation, and tight collaboration with teams across ML, simulation, and systems engineering. What You’ll DoBuild RL environments for tool control, workflow optimization, and long-horizon decision-makingDevelop safe and constrained RL methods, verifier-driven rewards, and offline to online training pipelinesCreate state/action representations and evaluation frameworks for reliable policy behaviorWork with cross-functional researchers and engineers to deploy RL agents into real workflowsWhat You BringStrong background in RL, optimal control, or sequential decision-makingExperience applying RL to complex simulated or physical systemsFamiliarity with safe/constrained RL, verifiers, or advanced evaluation pipelinesAbility to design environments, rewards, and diagnostics at scaleComfort working across ML, simulation, and systems interfaces

LLM Evaluations Engineering LeadSF Bay Area (Onsite) Full-time / Permanent We’re partnering with a deep-tech AI company building autonomous, agentic systems for complex physical and real-world environments. The team operates at the edge of what’s possible today, designing AI systems that plan, act, recover, and improve over long horizons in high-stakes settings. They’re hiring an LLM Evaluations Engineering Lead to own the evaluation, verification, and regression layer for agentic LLM systems running end-to-end workflows. This is not a metrics-only role. You’ll be building the guardrails that determine whether the system is actually getting better.Why this role mattersAs agentic LLM systems move into long-horizon planning and execution, evals become the bottleneck. This role defines whether:Agents are actually improvingChanges introduce silent regressionsUncertainty is shrinking or compounding“success” reflects real-world outcomes, not proxy metricsIf evals are wrong, everything downstream is wrong. This role sits directly on that fault line.What you’ll doBuild eval harnesses for agentic LLM systems (offline in-workflow)Design evals for planning, execution, recovery, and safetyImplement verifier-driven scoring and regression gatesTurn eval failures into training signals (SFT / DPO / RL)What they’re looking forStrong experience building evaluation systems for ML models (LLMs strongly preferred)Excellent software engineering fundamentals:PythonData pipelinesTest harnessesDistributed executionReproducibilityDeep understanding of agentic failure modes, including:Tool misuseHallucinated evidenceReward hackingBrittle formatting and schema driftAbility to reason about what to measure, not just how to measure itComfortable operating between research experimentation and production systemsWhy joinWork on frontier agentic AI systems with real-world consequencesOwn a foundational layer that determines system reliability and progressHigh autonomy, strong technical peers, and meaningful equityBuild evals that actually matter, not academic benchmarks

Senior Agentic AI EngineerA frontier AI company is building systems that can act in the physical world, experimenting, engineering, and executing multi-step processes with real-world constraints. Backed by major research funding and operating at the edge of physical-AI innovation, they’re creating capabilities that don’t exist anywhere else. Join to work from first principles, own high-impact systems end-to-end, and help define how agentic AI will operate complex workflows in the real world.Why This Role MattersBuild agent systems that plan, execute, and recover across intricate engineering workflowsShape foundational behaviour patterns for next-gen LLM tool-useJoin early enough to influence architecture, culture, and performance standardsWork on problems that sit far beyond typical “LLM app” engineeringWhat You’ll DoDevelop planners, state machines, and tool-calling flows using frameworks like LangGraphCreate schemas, action definitions, and cross-tool interfaces for reliable, traceable executionBuild error-handling, timeouts, retries, rollbacks, and replay mechanismsPartner with ML, infra, and systems teams to integrate agents into real engineering toolchainsWhat You BringStrong experience with agent systems, structured tool calling, or orchestration frameworksDeep intuition for schemas, deterministic execution, and multi-step workflow designAbility to model failure modes, edge cases, and safe interactions in complex systemsComfort working across AI, systems engineering, and specialised domain tools in a high-precision environment

Senior Agentic AI EngineerRemote (US-based)Full-time / Permanent We’re working with an AI-native company operating at the intersection of healthcare, insurance, and regulated enterprise systems. They’re building production-grade Agentic AI and LLM platforms that automate complex, high-impact decision workflows. They’re hiring a hands-on AI / LLM Lead to own the design, deployment, and evolution of autonomous, agent-driven systems in production.Why this role matters This is a core technical ownership role. You’ll be building AI systems that:Interpret and reason over complex documentsOrchestrate multi-step workflows autonomouslyDrive real decisions in regulated environmentsWhat you’ll doLead end-to-end AI & LLM system design from architecture to productionBuild agent pipelines using LangChain, LangGraph, and adjacent toolingDeploy and optimize open models using vLLM, TGI, and Python inference stacksFine-tune, evaluate, and integrate open-source LLMs (e.g. Llama, Mistral, Qwen)Design robust prompt, planning, and tool-execution strategiesBuild and operate large-scale embeddings retrieval pipelinesApply unsupervised ML (clustering, similarity, anomaly detection) on tabular dataWhat they’re looking forHands-on experience with Agentic AI & autonomous systemsProven track record deploying LLMs in productionExperience with vLLM, TGI, or similar model-serving stacksStrong experience with open-source LLMs (Llama, Mistral, Qwen, etc.)Experience with LangChain, LangGraph, or equivalent frameworksHands-on OCR experience (scanned PDFs, complex layouts)Solid understanding of chunking, embeddings, vector search, and retrievalIdeal mindsetOwns systems end-to-endComfortable making high-stakes technical decisionsThrives in fast-moving, ambiguous environmentsThinks in systems, not scripts