Machine Learning Engineer, Infra, AI for Drug Discovery

Roche•South San Francisco, CA

6d•$141,100 - $274,000

About The Position

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. At Roche’s AI for Drug Discovery (AI4DD) group (Prescient Design), we are building the machine learning platforms that enable researchers and engineers to move models from experimentation into reliable scientific and production workflows. We are seeking a Machine Learning Infrastructure Engineer to help build and operate the platforms that support model deployment, evaluation, promotion, monitoring, and lifecycle management across the organization. This role will contribute to our model-serving platform, and to the broader infrastructure required to make machine learning models easier to deploy, scale, observe, and safely incorporate into scientific and agentic workflows. The scope extends beyond LLM serving. You will work with a range of machine learning and scientific models, including real-time and batch inference workloads, GPU-backed services, agentic applications, and our in-silico drug discovery workflows. This is a hands-on engineering role for someone who enjoys writing and shipping production software across application code, cloud infrastructure, Kubernetes, and distributed systems. Prior inference-platform experience is helpful but not required; prior experience in biotech or drug discovery is also helpful but not required; we value strong engineering fundamentals, curiosity, and the ability to take platform problems from design through production operation.

Requirements

BS or MS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
3+ years of relevant industry experience in software engineering, infrastructure engineering, platform engineering, DevOps, MLOps, or a related area.
Strong Python programming skills and experience building and shipping maintainable production software, services, automation, or developer tooling.
A demonstrated interest in hands-on implementation and production software delivery.
Experience designing, deploying, or operating cloud systems (preferably on AWS) using services such as EKS, EC2, S3, IAM, SQS, SNS, and CloudWatch.
Experience with containers, Kubernetes, Helm, and IaC tools such as Terraform or Pulumi.
Experience with CI/CD, Git-based development workflows, automated testing, and software release practices.
Ability to troubleshoot complex systems using metrics, logs, traces, events, and observability tools such as Datadog, Prometheus, Grafana, or OpenTelemetry.
Understanding of distributed-systems concepts such as concurrency, queuing, retries, timeouts, idempotency, backpressure, and failure recovery.
Ability to gather requirements, communicate technical tradeoffs, and document systems for users and engineers with varied infrastructure experience.
Demonstrated ability to independently deliver practical, incremental solutions while considering immediate needs and longer-term platform direction.

Nice To Haves

Familiarity with model-serving or workflow-orchestration frameworks such as KServe, Triton, vLLM, Ray Serve, Prefect, or Dagster.
Experience optimizing model startup time, request throughput, batching, autoscaling, or GPU utilization.
Familiarity with model registries, experiment tracking, model evaluation, promotion workflows, or MLOps platforms.
Experience building event-driven systems using queues, event buses, or workflow orchestrators.
Familiarity with online and offline model evaluation, model-quality monitoring, data drift, or regression analysis.
Experience supporting scientific computing, high-performance computing, distributed training, or large-scale data processing.
Strong interest in the life sciences and drug discovery.

Responsibilities

Design, implement, ship, and operate scalable model-serving infrastructure for machine learning, scientific, LLM, and agentic workloads.
Help evolve our internal model deployment platform into a reliable, self-service platform for teams across the organization.
Improve platform scalability and reliability, including scale-to-zero, faster model startup, workload isolation, traffic management, and reduction of request failures and latency bottlenecks.
Build observability and operational tooling for model usage, latency, reliability, resource consumption, inference cost, bottlenecks, and service-level indicators.
Improve the usability of model deployment by developing validated configuration interfaces, reusable deployment patterns, APIs, command-line tools, and documentation.
Help converge real-time and batch inference workflows onto shared platform capabilities where appropriate.
Contribute to model lifecycle management infrastructure, including model registration and versioning, evaluation, promotion and release gates, monitoring, environment progression, and rollback.
Build event-driven integrations that connect model publication, evaluation, promotion, deployment, and retraining workflows.
Build consistent metrics and evaluation signals for understanding model cost, quality, reliability, and fitness for downstream workflows.
Partner with machine learning, data, scientific, and platform teams to translate requirements into maintainable solutions and remove infrastructure bottlenecks.
Own workstreams from design through implementation and production support, using strong software-engineering practices including testing, reviews, documentation, and incremental delivery.