{"id":1021090,"url":"https://alion.io/job/jpmorganchase-lead-software-engineer-machine-learning-platform","title":"Lead Software Engineer - Machine Learning Platform","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":535,"ghost_share":0.011,"stale_share":0.123,"repost_share":0.114,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":249000,"max_usd":509000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":691},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","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":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SFT","optional":false},{"name":"TensorFlow","optional":false},{"name":"Argo Workflows","optional":true},{"name":"Dask","optional":true},{"name":"Ray","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-10T16:53:44Z","employer_posted_date":"2026-08-10","last_verified_at":"2026-10-17T05:01:02Z","board_verified":true,"closed_at":null,"days_open":67,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":67},"description":"We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.\nAs a Lead Software Engineer at JPMorganChase within AI/ML Data Platforms, you are an integral part of an agile team that works to build and operate scalable, reliable ML training systems and pipelines on AWS and other cloud platforms. You will productionize training workloads (often GPU-based), improve performance and cost efficiency, and enable repeatable, well-governed training across environments.\nJob Responsibilities\nDesign, build, and maintain end-to-end ML training platform.\n\nRun and optimize GPU training workloads (single-node and distributed), improving throughput, utilization and reproducibility.\n\nBuild and operate training infrastructure on Kubernetes (e.g., EKS and other manage Kubernetes platforms), including resource management and workload troubleshooting.\n\nEnable Gen AI/LLM training and fine-tuning workflows (e.g., supervised fine-tuning), including evaluation harnesses, artifact/version governance, and scalable GPU execution patterns aligned to enterprise controls.\n\nImplement observability for training systems: metrics, logs, dashboards, alerting, and operational runbooks.\n\nPartner with data engineering and platform teams to define interfaces, standards, and guardrails (security, access, cost controls)\n\nImprove developer experience for training: standardized containers, CI/CD, templates, documentation, and self-service workflow\n\nDrives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.\n\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.\n\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 5+ years applied experience.\n\nDemonstrated experience running ML training in cloud environments and debugging issues across infrastructure & code.\n\nStrong Python skills with solid engineering practices (testing, code reviews, modular design, dependency management).\n\nExperience building automation/CI for ML codebases (build, test, release, deployment/promotion workflows).\n\nHands on experience with deep learning training workflows and at least one major framework (eg., PyTorch or TensorFlow).\n\nUnderstanding of training performance and stability: data loading bottlenecks, mixed precision, checkpointing, reproducibility, and evaluation methodology.\n\nExperience with distributed training and related concepts (e.g., DDP/FSDP/DeepSpeed concepts, collective communication basics, scaling and bottleneck analysis).\n\nAbility to profile and optimize training systems (CPU/GPU utilization, memory, I/O throughput, networking, scheduling).\n\nExperience with Kubernetes fundamentals for running compute-intensive workloads and AWS (eg., EKS/ECR, S3, IAM, VPC/networking, Cloudwatch, EC2)\n\nDemonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.\n\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.\n\nPreferred qualifications, capabilities, and skills\nExperience running training workloads across multiple cloud platforms and managing portability, performance, and governance across environments.\n\nFamiliarity with cloud-native networking/storage patterns for high-throughput training and artifact management.\n\nExperience optimizing training input pipelines (sharding, prefetching, caching, format choices such as Parquet/WebDataset) and working with large datasets.\n\nFamiliarity with distributed compute frameworks (Spark, Ray, Dask) for feature/dataset generation.\n\nFamiliarity with workflow orchestration tools (Airflow-like systems, Argo Workflows-like patterns) and model registry concepts.\n\nExperience optimizing training cost/performance (right-sizing, scheduling policies, interruptible capacity strategies where applicable budge guardrails, quota planning).\n\nStrong observability practice for training systems: metrics/logs/traces, GPU telemetry, dashboards, and alert tuning.\n\nFEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries.","description_format":"text","description_chars":5174,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Wealth Management & Financial Advisors","Asset Management & Funds","Investment Banking & M&A Advisory"],"lifecycle":[{"event":"open","at":"2026-09-18T05:52:23Z"},{"event":"close","at":"2026-09-18T12:15:01Z"},{"event":"reopen","at":"2026-09-19T01:13:17Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 3,429 · green card filings: 933","filings_12m":3429,"filings_prev_12m":3601,"green_card_filings_12m":933,"median_offered_wage_usd":160366,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+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":2086}],"liveness":{"score":7,"band":"cold","label":"Long shot","p_open":1,"p_active":0.245,"p_room":0.28,"age_days":67,"expected_fill_days":8,"reasons":["conf:0","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/jpmorganchase-lead-software-engineer-machine-learning-platform","json_url":"https://alion.io/job/jpmorganchase-lead-software-engineer-machine-learning-platform.json","meta":{"generated_at":"2026-10-17T06:26:23Z","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":8717,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"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":257},"rest":"https://alion.io/mcp/rest/get_company?id=257"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fjpmorganchase-lead-software-engineer-machine-learning-platform"},{"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%2Fjpmorganchase-lead-software-engineer-machine-learning-platform"},{"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%2Fjpmorganchase-lead-software-engineer-machine-learning-platform"},{"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/jpmorganchase-lead-software-engineer-machine-learning-platform\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fjpmorganchase-lead-software-engineer-machine-learning-platform"}]}