Machine Learning Engineer I/II, Applied AI

Location: Cambridge, MA, USA or San Francisco, CA, USA

Expected base salary: $116,000–$170,000 USD per year

Company

Building scientific superintelligence by combining advanced AI models with proprietary instruments to automate and accelerate scientific discovery across medicine, materials, and energy.

What you will do

• Adapt AI models to customer-specific scientific workflows and close the gap between model capabilities and practical use cases.
• Post-train models using SFT and reinforcement learning approaches such as DPO, PPO, and GRPO.
• Build evaluation loops, reusable tooling, monitoring, and quality workflows for model adaptation and deployment.
• Design experiments and feed customer feedback, data signals, and evaluation results into model improvement cycles.
• Collaborate with AI researchers and software engineers to integrate model behavior into production product workflows.
• Debug model failures using traces, evaluations, logs, customer context, and scientific feedback.

Requirements

• Experience building, training, adapting, or evaluating machine learning models.
• Strong software engineering skills in Python and modern machine learning frameworks such as PyTorch, JAX, or TensorFlow.
• Experience designing experiments, evaluation metrics, or test sets for model performance.
• Ability to debug model behavior using data, traces, logs, and qualitative feedback.
• Experience collaborating across research and engineering teams to move ML capabilities into usable systems.
• Familiarity with large language models, multimodal models, or agentic AI systems, plus clear technical communication skills.

Nice to have

• Experience adapting models for customer-facing or production workflows.
• Experience with scientific, technical, or data-intensive customer use cases.
• Familiarity with retrieval-augmented generation, tool use, agentic workflows, RL post-training, or MoE architectures.
• Experience building evaluation harnesses, model monitoring, or quality dashboards.
• Experience working with product or customer-facing teams to translate needs into ML improvements.

Culture & Benefits

• Early-stage environment with autonomy, flexibility, and access to compute for frontier science problems.
• Full-time U.S. employees receive medical, dental, and vision coverage, life and disability insurance, flexible time off, and company holidays.
• Benefits also include paid parental leave, educational assistance, commuter benefits, and subsidized lunches for office-based employees.
• International full-time employees receive benefits tailored to their region.
• Bonus potential and early-stage equity are available.