Machine Learning Infrastructure Engineer
Hybrid (San Mateo, CA), Hybrid (New York, NY), Hybrid (Remote)$0Full-timePosted 3mo ago
About the role
Role focused on building and optimizing the data and orchestration infrastructure for molecular AI, spanning in-house workflow orchestration and data preprocessing pipelines for proteins, chemicals, and ML models.
Responsibilities
- Design and evolve our in-house workflow orchestration framework, including DAG construction and execution, dependency management, scheduling, caching, retries, observability, and distributed execution.
- Build and optimize large-scale preprocessing pipelines for protein structures, chemical datasets, simulations, and machine learning training data.
- Profile end-to-end workflows and aggressively eliminate bottlenecks—from redundant I/O and serialization to unnecessary recomputation and poorly parallelized workloads.
- Develop abstractions for lazy execution, incremental computation, intelligent caching, and artifact reuse so expensive scientific computations happen only when necessary.
- Partner closely with ML researchers, computational chemists, and scientific software engineers to translate complex research workflows into scalable, reproducible computational pipelines.
- Improve the developer experience for scientists and engineers authoring pipelines, making sophisticated distributed workflows intuitive to define, debug, monitor, and extend.
- Make architectural decisions spanning local and distributed execution, storage, compute scheduling, data lineage, and reproducibility.
- Use lessons from real-world scientific pipelines to continuously improve the orchestration platform itself—and use improvements to the platform to unlock faster, more ambitious scientific workflows.
Qualifications
- Passionate about DAGs. You naturally think about computation as graphs of dependencies and care deeply about how work is scheduled, parallelized, cached, retried, and recomputed.
- Impatient about latency. When a pipeline takes hours, your instinct is to understand exactly where the time went and systematically make it faster.
- An evangelist for lazy execution and caching. You dislike unnecessary work and look for principled ways to avoid recomputation, move less data, and reuse intermediate results.
- A strong systems engineer. You are comfortable reasoning across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance.
- Hands-on with data-intensive systems. You have built production pipelines or infrastructure that processes large datasets reliably and efficiently.
- Comfortable moving between framework and application layers. You're as interested in designing the orchestration primitive as you are in optimizing the pipeline built with it.
- A pragmatic abstraction builder. You can identify the common pattern hiding underneath many specialized workflows without forcing every scientific problem into an overly generic framework.
- A strong collaborator with researchers. You can understand an evolving scientific workflow, identify its computational structure, and turn it into robust infrastructure without slowing down experimentation.
- Energized by unusual problems. You enjoy environments where off-the-shelf infrastructure gets you 80% of the way there—and the interesting work is designing the remaining 20%.
Skills
Required:In-house workflow orchestration framework designLarge-scale data preprocessing pipelinesPerformance profiling and bottleneck eliminationDistributed systems & orchestrationCollaboration with researchers
Nice to have:Lazy execution and caching
Benefits
- Competitive compensation package that includes salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at our offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
Last updated Aug 17, 2026. Interested? Apply on the company site.
About Genesis Molecular AI
The frontier AI for drug discovery
Genesis pairs frontier AI with world-class drug hunters to discover small molecule medicines. Built on Pearl and GEMS. Partnered with Gilead and Incyte.
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