{"id":4186071,"url":"https://alion.io/job/weekday-ml-infrastructure-and-performance-engineer-ai-expert-remote","title":"ML Infrastructure and Performance Engineer, AI Expert (Remote)","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":80,"open_postings":117,"ghost_share":0,"stale_share":0.983,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-11-12T10:38:37Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":90,"max":120,"currency":"USD","period":"hour","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS Trainium","optional":false},{"name":"CUDA","optional":false},{"name":"CUTLASS","optional":false},{"name":"JAX","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"PyTorch","optional":false},{"name":"SGLang","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"TPU","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"XLA","optional":false},{"name":"FSDP","optional":true},{"name":"Megatron-LM","optional":true},{"name":"MLIR","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-11-12T18:17:58Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T18:17:58Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role is for one of our clients\nCompensation: $90 - $120 per hour\nLocation: Remote\nJob Overview\nWe are seeking an experienced ML Infrastructure and Performance Engineer to contribute to the development and advancement of next-generation Generative AI systems. This role is focused on building, optimizing, debugging, and evaluating the infrastructure that powers large language models and other frontier AI systems.\nThe ideal candidate will have hands-on experience in ML systems engineering, accelerator performance optimization, distributed workloads, or large-scale model serving. Expertise in areas such as custom accelerator kernels, performance profiling, trace analysis, distributed systems debugging, and high-throughput inference is particularly valuable. Experience with JAX, XLA, and TPU-based infrastructure will be a strong advantage.\nIn this role, you will apply your engineering expertise to create, evaluate, and improve technical tasks and solutions used to train and assess advanced AI models. Your work will help improve how frontier AI systems understand and solve complex ML infrastructure and performance engineering problems.\nThis is a fully remote opportunity open to candidates worldwide. The engagement will be structured in accordance with applicable local employment and compliance requirements. Candidates must be available for a full-time commitment of 40 hours per week, with no conflicting professional engagements.\nRequirements\nKey Responsibilities\nDesign challenging technical tasks covering accelerator kernel development, performance profiling, distributed workload debugging, and high-throughput inference serving.\nDevelop complex and adversarial engineering problems that test the capabilities of advanced AI models, including tasks involving accelerator programming and optimization.\nCreate accurate, technically sound reference solutions that demonstrate best practices in ML systems engineering.\nEvaluate AI-generated solutions for correctness, efficiency, scalability, and practical applicability.\nDevelop detailed technical guidelines and assessment rubrics for kernel optimization, profiler interpretation, distributed systems, and inference performance.\nAnalyze performance bottlenecks across ML infrastructure, compilers, accelerators, and model-serving systems.\nProvide clear, structured technical feedback to help improve AI model capabilities in ML systems and infrastructure engineering.\nCollaborate with researchers and fellow subject matter experts to maintain the quality, consistency, and accuracy of AI training and evaluation data.\nRequired Qualifications\nAt least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, accelerator performance engineering, or a closely related field.\nPractical experience in at least one of the following areas:\nAccelerator Kernel Programming: Developing or optimizing custom kernels using Pallas, Triton, CUDA, CUTLASS, or similar technologies.\nPerformance Profiling: Working with profiling and trace-analysis tools such as JAX/XLA Profiler, Kineto, torch.profiler, or NVIDIA Nsight.\nDistributed Workload Debugging: Identifying and resolving performance and execution issues in distributed or accelerator-intensive workloads.\n LLM Inference Serving: Experience with vLLM, SGLang, TensorRT-LLM, JetStream, KV cache optimization, paged attention, or continuous batching.\nProduction-level experience with JAX and/or PyTorch.\nUnderstanding of modern accelerator architectures, including TPUs (v4-v6e), NVIDIA H100/B200, AWS Trainium, or AMD MI300.\nAbility to analyze and optimize throughput, latency, memory utilization, and computational efficiency.\nDemonstrable professional growth and career progression in engineering or infrastructure-focused roles.\nAvailability to commit at least 40 hours per week on weekdays.\nExcellent written communication skills, with the ability to explain complex technical concepts and engineering decisions clearly.\nPreferred Qualifications\nExperience with JAX, XLA, and TPU-based development environments.\nKnowledge of custom operators, sharding, and distributed training frameworks such as pjit, shard_map, FSDP, Megatron, or MaxText.\nFamiliarity with compiler and graph-level technologies such as XLA, StableHLO, torch.compile, and MLIR.\nExperience developing performance benchmarks, technical evaluation frameworks, or engineering assessment rubrics.\nExposure to AI model evaluation, synthetic training data generation, or working with frontier AI systems.\nEngagement Details\nWork Arrangement: Fully Remote\nGeographic Eligibility: Worldwide\nCommitment: Full-Time, 40 hours per week\nSchedule: Weekdays\nEngagement Structure: Employment or engagement through an appropriate local partner, depending on the candidate's country of residence.\n Availability: Candidates must be able to commit exclusively to this engagement without conflicting professional commitments.","description_format":"text","description_chars":4927,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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