Machine Learning Engineer, Infra, AI for Drug Discovery

Roche

Machine Learning Engineer developing scalable infrastructure for AI drug discovery at Roche. Building platforms for model deployment, evaluation, and monitoring.

Posted 7/31/2026full-timeSouth San Francisco • California, New York • 🇺🇸 United StatesMid-LevelSenior💰 $147,600 - $274,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating scalable model-serving infrastructure, with strong proficiency in Python programming and cloud systems, particularly on AWS. Capable of improving platform reliability and usability through effective model lifecycle management and observability practices.

Highest-signal resume keywords

Python ProgrammingCloud Systems (AWS)Kubernetes and ContainersCI/CD and Automated TestingModel Lifecycle Management

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringInfrastructure EngineeringPlatform EngineeringMLOpsEvent-Driven SystemsModel EvaluationDistributed Systems ConceptsObservability ToolsModel-Serving FrameworksPerformance Optimization

Soft Skills

CommunicationProblem-SolvingDocumentationCollaborationIndependent Delivery

Tools & Technologies

AWS Services (EKS, EC2, S3, IAM, SQS, SNS, CloudWatch)TerraformPulumiDatadogPrometheusGrafanaOpenTelemetryGitHelmCI/CD Tools

Industry Keywords

Machine LearningScientific ComputingHigh-Performance ComputingDrug DiscoveryData Processing

Tech Stack

Tools & technologies

AWSCloudEC2GrafanaKubernetesPrometheusPythonRayTerraform

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
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.
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.

Benefits

Comp & perks

A discretionary annual bonus may be available based on individual and Company performance.
This position also qualifies for the benefits detailed at the link provided below.