{"id":3234412,"url":"https://alion.io/job/genentech-machine-learning-engineer-infra-ai-for-drug-discovery","title":"Machine Learning Engineer, Infra, AI for Drug Discovery","company":{"id":1757810,"name":"Genentech","domain":"gene.com","url":"https://alion.io/company/gene-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":31,"ghost_share":0,"stale_share":0.935,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["South San Francisco, United States","New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":147600,"max":274000,"currency":"USD","period":"year","gross":null,"usd_annual":274000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Datadog","optional":false},{"name":"Git","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"IAM","optional":false},{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prefect","optional":false},{"name":"Prometheus","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"Ray Serve","optional":false},{"name":"Terraform","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"Ray","optional":true}],"status":"live","first_seen_at":"2026-08-05T00:00:00Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-11-01T00:10:11Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":88},"description":"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.\nAdvances 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.\nThe Opportunity\nAt 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.\nThe 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.\nIn this role, you will:\nDesign, implement, ship, and operate scalable model-serving infrastructure for machine learning, scientific, LLM, and agentic workloads.\n\nHelp evolve our internal model deployment platform into a reliable, self-service platform for teams across the organization.\n\nImprove platform scalability and reliability, including scale-to-zero, faster model startup, workload isolation, traffic management, and reduction of request failures and latency bottlenecks.\n\nBuild observability and operational tooling for model usage, latency, reliability, resource consumption, inference cost, bottlenecks, and service-level indicators.\n\nImprove the usability of model deployment by developing validated configuration interfaces, reusable deployment patterns, APIs, command-line tools, and documentation.\n\nHelp converge real-time and batch inference workflows onto shared platform capabilities where appropriate.\n\nContribute to model lifecycle management infrastructure, including model registration and versioning, evaluation, promotion and release gates, monitoring, environment progression, and rollback.\n\nBuild event-driven integrations that connect model publication, evaluation, promotion, deployment, and retraining workflows.\n\nBuild consistent metrics and evaluation signals for understanding model cost, quality, reliability, and fitness for downstream workflows.\n\nPartner with machine learning, data, scientific, and platform teams to translate requirements into maintainable solutions and remove infrastructure bottlenecks.\n\nOwn workstreams from design through implementation and production support, using strong software-engineering practices including testing, reviews, documentation, and incremental delivery.\n\nWho You Are\nBS or MS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.\n\n3+ years of relevant industry experience in software engineering, infrastructure engineering, platform engineering, DevOps, MLOps, or a related area.\n\nStrong Python programming skills and experience building and shipping maintainable production software, services, automation, or developer tooling.\n\nA demonstrated interest in hands-on implementation and production software delivery.\n\nExperience designing, deploying, or operating cloud systems (preferably on AWS) using services such as EKS, EC2, S3, IAM, SQS, SNS, and CloudWatch.\n\nExperience with containers, Kubernetes, Helm, and IaC tools such as Terraform or Pulumi.\n\nExperience with CI/CD, Git-based development workflows, automated testing, and software release practices.\n\nAbility to troubleshoot complex systems using metrics, logs, traces, events, and observability tools such as Datadog, Prometheus, Grafana, or OpenTelemetry.\n\nUnderstanding of distributed-systems concepts such as concurrency, queuing, retries, timeouts, idempotency, backpressure, and failure recovery.\n\nAbility to gather requirements, communicate technical tradeoffs, and document systems for users and engineers with varied infrastructure experience.\n\nDemonstrated ability to independently deliver practical, incremental solutions while considering immediate needs and longer-term platform direction.\n\nPreferred\nFamiliarity with model-serving or workflow-orchestration frameworks such as KServe, Triton, vLLM, Ray Serve, Prefect, or Dagster.\n\nExperience optimizing model startup time, request throughput, batching, autoscaling, or GPU utilization.\n\nFamiliarity with model registries, experiment tracking, model evaluation, promotion workflows, or MLOps platforms.\n\nExperience building event-driven systems using queues, event buses, or workflow orchestrators.\n\nFamiliarity with online and offline model evaluation, model-quality monitoring, data drift, or regression analysis.\n\nExperience supporting scientific computing, high-performance computing, distributed training, or large-scale data processing.\n\nStrong interest in the life sciences and drug discovery.\n\nRelocation benefits are NOT available for this job posting\nThe 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.\nBenefits\n#tech4lifeAI\nGenentech 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.\nIf 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.","description_format":"text","description_chars":7521,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Drug Discovery","Biotechnology","Health Care","Care Coordination & Population Health"],"lifecycle":[{"event":"open","at":"2026-10-25T10:25:27Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 188 · green card filings: 34","filings_12m":188,"filings_prev_12m":151,"green_card_filings_12m":34,"median_offered_wage_usd":150788,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":40}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.271,"p_room":0.28,"age_days":87,"expected_fill_days":21,"reasons":["conf:4","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-31T05:58:55Z"},"pay":{"stated_usd_annual":274000,"is_top_pay":true},"html_url":"https://alion.io/job/genentech-machine-learning-engineer-infra-ai-for-drug-discovery","json_url":"https://alion.io/job/genentech-machine-learning-engineer-infra-ai-for-drug-discovery.json","meta":{"generated_at":"2026-11-01T06:01:44Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":9407,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-02T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1757810},"rest":"https://alion.io/mcp/rest/get_company?id=1757810"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgenentech-machine-learning-engineer-infra-ai-for-drug-discovery"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fgenentech-machine-learning-engineer-infra-ai-for-drug-discovery"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fgenentech-machine-learning-engineer-infra-ai-for-drug-discovery"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/genentech-machine-learning-engineer-infra-ai-for-drug-discovery\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgenentech-machine-learning-engineer-infra-ai-for-drug-discovery"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3234412}},{"tool":"search_jobs","arguments":{"company":"gene.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3234412},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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