About the role:We're hiring a Senior / Principal ML Engineer to build and own the digital infrastructure that supports Merge's diverse computational workloads. You'll design the distributed-training & inference, experiment-tracking, and deployment frameworks that enable data scientists to rapidly iterate on models - spanning de-novo molecular design, biophysical modeling, signal processing, and computer vision. You'll architect systems that translate research prototypes to production grade. This is a horizontal, highly-leveraged role - success means empowering every computational scientist to move faster, with more rigor and less friction.
In this role, you will:- Build the scientific and engineering scaffolding for active-learning and closed-loop optimization, including data ETL, ML modeling, and library design.
- Collaborate with computational scientists to define tractable optimization objectives and encode domain specific priors and constraints.
- Implement model registries, evaluation frameworks, and automated reporting for benchmarking and experiment comparison.
- Define CI/CD pipelines, resource orchestration (Kubernetes, Ray, or Slurm)
- Define and own the ML engineering roadmap, mentoring other computational scientists and establishing best practices for code hygiene, testing, and reproducibility.
You might thrive in this role if you have:- Deep experience in ML infrastructure, systems engineering, and production ML workflows (training 14 deployment 14 monitoring).
- Proficiency with Python, PyTorch, JAX, Ray, Kubernetes, and cloud services (AWS / GCP / Azure).
- Deep experience with experiment-tracking and model-management tools (MLflow, Weights & Biases, DVC).
- Strong grounding in software engineering fundamentals - version control, modular design, CI/CD, and distributed computing
- A systems-level mindset: you think in terms of model lifecycle, not just single scripts.
- Experience bridging machine learning and experimental science-working with sparse, noisy, and or high-cost data.
- A collaborative, systems-level mindset:
- Nice to have: familiarity with neuroscience
If you're excited about this role but don't meet every qualification, please apply. As we build, we're hiring for complementary strengths to form a high-impact team.
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