• Collaborate with other engineering teams and cross functional teams to translate ambiguous business problems into a clear, prioritized ML product roadmap for your team. • Have a 'player-coach' mentality, and contribute hands-on technical expertise while providing strategic direction and mentorship to the team. • Establish a high-performing engineering setup that enables the team to rapidly iterate, experiment, and deploy models, fostering a culture of operational excellence and close partnership with product and business stakeholders. • Drive continuous improvement in the team's processes, tooling, and infrastructure to optimize for velocity, quality, and maintainability. • Actively monitor the ML and AI landscape to ensure the team is continuously up-to-speed on state-of-the-art research, techniques, and tools, promoting their strategic adoption where appropriate. • Recruit, manage, and develop a diverse and highly-skilled team of Machine Learning Engineers, setting clear goals and expectations, and providing timely and actionable feedback. • Define and track key performance indicators (KPIs) to measure the impact and success of the team’s ML products, communicating results clearly to leadership and stakeholders. • Manage project timelines, dependencies, and risks, ensuring timely delivery of high-quality, reliable, and scalable ML solutions.

• Perform rapid diagnosis across model, data, code, infrastructure, and evaluation layers for blocked or unstable efforts. • Identify root causes and define corrective actions required to restore progress. • Communicate findings and resolution plans clearly across research, engineering, and operational teams. • Contribute directly to blocked ML initiatives by implementing fixes across model behavior, data pipelines, and system architecture. • Develop and validate solutions, including debugging, targeted refactoring, and experimental validation. • Ensure that resolved systems are stable, validated, and ready for continued development. • Provide clear handoff artifacts, including working code, documentation, and recommended next steps. • Work across research, infrastructure, platform, evaluation, and integration teams to align on root causes and resolution plans.

Role Description We are seeking a Senior / Staff Machine Learning Scientist to work across the breadth of Chemify's platform — generative models for chemistry, search and planning for retrosynthesis, computer vision for telemetry from our robotic systems, and agentic workflows that tie it all together. You will partner with computational chemists, CADD scientists, software engineers, and hardware engineers, and apply AI/ML to build the next generation of Chemify's platform. What sets this role apart is the combination of breadth of ML problems — generative chemistry, vision, search, agents — paired with a robotic platform that turns your models into physical experiments. If working across a wide range of hard ML problems on a real-world platform sounds like the right shape of job for you, we'd love to welcome you to our team. Key Responsibilities - Build generative and foundation chemistry models for molecular design. - Advance retrosynthesis and synthesis-aware ML by leveraging Chemify's reaction database and robot-execution data. - Apply computer vision to transform robot telemetry into models that monitor process state and feedback into experimental control. - Prototype agentic workflows that orchestrate models, tools, and the platform — closing loops between proposal, execution, observation, and learning. - Productionise models into a reproducible, API-first toolkit; partner with Infrastructure on GPU training and HPC; maintain high standards of ML best practices, including rigorous evaluation, benchmarks, and reproducibility. - Mentor junior ML scientists, partner with the Head of Advanced Machine Learning on hiring and growth, and represent Chemify's AI/ML capability externally. - (Staff level) Set technical direction across the AI/ML stack; lead cross-cutting initiatives spanning chemistry models, retrosynthesis, vision, and agents. Qualifications - PhD or equivalent experience in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field. - 5+ years (Senior) or 8+ years (Staff) of hands-on applied ML experience, including production-grade work. - Deep familiarity with modern deep learning stack (PyTorch or JAX). - Breadth across at least two of: generative models (diffusion, autoregressive, flow-based), graph and equivariant networks, vision (CNNs, ViTs, multimodal LLMs), search and planning (MCTS, A*), or agentic / RL systems. - Experience taking ML from prototype to production: reproducible pipelines, distributed jobs, and batch workflows on cloud (AWS / GCP / Azure) or HPC. - Strong scientific computing instincts: clean Python, careful experiment design, leakage-aware splits, and rigorous benchmarks. - Clear communication with non-ML scientists and engineers and a willingness to pick up new domains (you don't need to know chemistry on day one). - (Staff level) A track record of technical leadership: mentoring, setting standards, and influencing scientific and technical direction beyond your own projects. Beneficial Skills - Practical experience with active learning, Bayesian optimisation, conformal prediction, or uncertainty quantification in iterative real-world loops. - Familiarity with retrosynthesis ML, computer-aided synthesis planning (CASP), or reaction-condition / yield prediction. - Working knowledge of how ML fits into a drug-discovery or materials-design workflow, plus familiarity with cheminformatics tooling (e.g. RDKit, OpenEye) — or willingness to pick these up. - MLOps fluency: experiment tracking, data versioning, model serving, and observability of deployed models. - A visible track record in the field — peer-reviewed publications, open-source contributions, or public projects that demonstrate your judgement on real ML problems. Benefits - Impact: Your models will directly shape what Chemify's robotic platform proposes, plans, and observes — at a company uniquely positioned to close the loop between design and physical experiment. - Autonomy: Reporting to the Head of Advanced Machine Learning, you will work across the AI/ML problems with the most impact, and have meaningful influence over the direction of our AI/ML capability. - Ambition: We are a Series B deep-tech company investing in world-class infrastructure and tackling problems at the frontier of AI, robotics, and chemistry. You will have the resources and the mandate to do AI/ML in a way that isn't possible elsewhere — across chemistry, vision, search, and autonomous systems on the same platform.

• Collaborate with partner ML and annotation engineers and TPMs to specify infrastructure and training requirements. • Design and maintain robust CI/CD and CT (Continuous Training) pipelines for complex multimodal models. • Implement versioning and storage strategies for large-scale 2D/3D datasets to ensure reproducibility and high-throughput access. • Deploy and operate systems for monitoring model performance and detecting data drift in production environments.