{"id":176002,"url":"https://alion.io/job/avride-software-engineer-ml-platform","title":"Software Engineer – ML Platform","company":{"id":63122,"name":"Avride","domain":"avride.ai","url":"https://alion.io/company/avride","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":20,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Austin, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":136000,"max_usd":297000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Argo Workflows","optional":false},{"name":"C++","optional":false},{"name":"Go","optional":false},{"name":"Kubernetes","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Linux","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-04-01T18:02:35Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-16T12:46:37Z","board_verified":true,"closed_at":null,"days_open":197,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":197},"description":"About the team\nThe ML Platform team at Avride builds the infrastructure that powers large-scale ML training and data processing for autonomous driving. We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute, and production-grade tooling for the full model lifecycle.\nAbout the role\nAs an ML Platform Engineer at Avride, you'll own critical pieces of the ML stack: workflow orchestration, distributed execution, resource governance, performance.You will shape how ML teams across the company run experiments and train models at scale. You will build the abstractions and services that make training workloads reliable, cost-efficient, and fast, helping ML teams run at scale on Kubernetes with strong reliability and excellent developer experience.\nWhat you will do\nBuild and scale our ML compute platform on Kubernetes, using Argo Workflows for training, evaluation, and data processing orchestration\nDesign and implement core platform capabilities, including a Ray-based internal SDK for distributed execution, and multi-tenant resource governance - scheduling, priorities, quotas, and policy enforcement across GPU, CPU, memory, and IO\nImprove end-to-end training throughput and platform efficiency by optimizing data access patterns, caching, and removing bottlenecks in storage, network, and resource contention\nWork directly with ML teams to debug complex workload issues, drive root-cause analysis, and turn recurring problems into platform-level fixes\nEvaluate, integrate and extend open-source tooling (Argo Workflows, Ray, Kubernetes ecosystem) to meet evolving platform needs\nWhat you will need\nStrong proficiency in Python or Go; C++ is a plus\nTrack record of designing and building scalable, maintainable systems and services\nExperience operating production services end-to-end: APIs, reliability practices, observability\nDeep knowledge of Kubernetes: how scheduling, resource management, controllers, and pod lifecycle actually behave under pressure\nSolid Linux and systems debugging skills: performance investigation, networking, storage/IO\nAbility to troubleshoot complex production issues across logs, metrics, and traces and drive them to resolution\nNice to have\nExperience with Argo Workflows, Ray, MLflow, or comparable distributed ML tooling\nHands-on experience building or operating large-scale ML training systems: GPU scheduling, distributed training, training data pipelines\nTrack record of optimizing resource usage and performance in distributed environments\nCandidates are required to be authorized to work in the U.S. The employer is not offering relocation, sponsorship, and remote work options are not available.\nAvride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. 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