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About This Role
### The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
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.
The Opportunity
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.
In this role, you will:
- 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.
Who You Are
- 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.
Preferred
- 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.
Relocation benefits are NOT available for this job posting
The expected salary range for this position based on the primary location of California is $147,600, \- $274,000 and for New York, $141,100 \- 262,100\. Actual pay will be determined based on experience, qualifications, geographic location, and other job\-related factors permitted by law. 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.
Benefits
\#ComputationCoE
\#tech4lifeComputationalScience
\#tech4lifeAI
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
Salary Context
This $141K-$274K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Genentech, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $141K to $274K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Genentech AI Hiring
Genentech has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $207K - $312K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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