{"id":701844,"url":"https://alion.io/job/inferact-inference-engineering-co-op","title":"Inference Engineering, Co-op","company":{"id":678585,"name":"Inferact","domain":"inferact.ai","url":"https://alion.io/company/inferact","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":85,"open_postings":25,"ghost_share":0,"stale_share":0.6,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":225000,"max_usd":500000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUTLASS","optional":false},{"name":"Go","optional":false},{"name":"InfiniBand","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"MLIR","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NCCL","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"Rust","optional":false},{"name":"TPU","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"XLA","optional":false},{"name":"Helm","optional":true},{"name":"PyTorch","optional":true},{"name":"Ray","optional":true},{"name":"SGLang","optional":true},{"name":"SLURM","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-09-10T18:54:02Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-02T19:11:59Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":52},"description":"Overview\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\nAbout the Role\nWe're looking for exceptional University of Waterloo co-op students who want to work on the systems that determine how fast, efficiently, and reliably frontier AI models run on frontier workloads at scale. This is not a sandboxed internship project. We will match your strengths and interests to a real engineering problem across the vLLM stack, from model execution and low-level accelerator code to distributed serving and the cloud platform that makes it all usable.\nYou'll work alongside the creators and core maintainers of vLLM on work intended to ship into open source, production systems, or the tooling that supports both. You will have a primary technical track, meaningful ownership, and mentorship from a small, senior team, with opportunities to collaborate across models, compilers, accelerators, networking, and distributed systems. The goal is to let exceptional students learn at the frontier while making contributions used by developers and AI teams around the world.\nPotential Focus Areas\nYour co-op will have a primary home in one of the following tracks, with opportunities to contribute across others:\nInference Runtime: Bring new model architectures and inference techniques to life in vLLM. Implement ideas from research papers; support mixture-of-experts, multimodal, diffusion, and agentic workloads; and improve scheduling, continuous batching, KV-cache memory management, prefix caching, and hybrid model serving.\n\nPerformance & Scale: Build the distributed serving data plane that lets vLLM run across many GPUs and nodes. Work on tensor, expert, or context parallelism; prefill/decode separation and KV-cache transport; fault tolerance and multi-tenancy; and high-performance communication using NCCL, DeepEP, NVSHMEM, RDMA, or InfiniBand.\n\nKernel Engineering: Raise the hardware performance ceiling by writing and optimizing attention, GEMM, sampling, KV-cache, fused, and quantization kernels. Use CUDA, Triton, TileLang, CUTLASS/CUTE, or related tools; reason about memory hierarchy, occupancy, and tensor cores; and prove speedups through profiling, correctness tests, and reproducible benchmarks.\n\nAMD GPU Performance: Help make vLLM first-class on AMD accelerators. Work across ROCm, HIP, Triton, CK, AITER, kernels, runtime paths, quantization, compiler integration, and performance-regression infrastructure while learning how AMD-specific execution, memory, and toolchain constraints shape inference.\n\nTPU Performance: Help make vLLM fast and correct on Google TPUs. Build backend, runtime, and compiler integrations with JAX, XLA, Pallas, MLIR, and related tooling; inspect compiler artifacts; work on lowering, fusion, and code generation; and benchmark production-relevant serving across correctness, latency, and throughput.\n\nCloud Orchestration: Build the operational platform that makes large-scale inference deployable and reliable. Work on Kubernetes and custom operators, topology-aware GPU scheduling, zero-downtime vLLM rollouts, token-aware routing, observability, infrastructure-as-code, automated recovery, and bring-your-own-cloud or multi-cloud fleet management.\n\nYou are not expected to arrive with experience in every track. We care most about unusual depth or learning velocity in one area, strong fundamentals, and evidence that you can turn a difficult problem into working, measurable software.\nSkills and Qualifications\nMinimum qualifications:\nCurrently pursuing a bachelor's, master's, or PhD degree in computer science, engineering, mathematics, or a related technical field, and eligible for a University of Waterloo co-op work term.\n\nStrong programming ability in Python, C++, Rust, Go, or another systems-oriented language.\n\nStrong computer science fundamentals and the ability to learn from research papers, technical documentation, and complex systems code.\n\nEvidence that you have built and debugged nontrivial software through coursework, research, a prior internship, open-source work, or an ambitious side project.\n\nA builder mindset: you define what success means, measure results, validate correctness, communicate clearly, and keep iterating until the system works.\n\nPreferred qualifications:\nDepth in at least one relevant area such as ML or inference systems, distributed systems, GPU or accelerator programming, compilers, high-performance computing, operating systems, networking, Kubernetes, or cloud infrastructure.\n\nHands-on experience with one or more relevant technologies such as PyTorch, vLLM, SGLang, TensorRT-LLM, CUDA, Triton, TileLang, ROCm/HIP, JAX/XLA, Pallas, Kubernetes, Helm, Terraform, Ray, or SLURM.\n\nExperience profiling or benchmarking software, validating numerical or systems correctness, or building tests that protect against performance regressions.\n\nExperience with systems beyond a single local process or device, including multi-GPU, multi-node, research-cluster, high-throughput, or production-like workloads.\n\nBonus points if you have:\nContributed to open-source ML, systems, compiler, or infrastructure projects, especially vLLM or adjacent projects.\n\nBuilt a research system, benchmark suite, compiler or kernel project, distributed service, or infrastructure tool that demonstrates unusual technical depth and initiative.\n\nWorked across more than one layer of the stack and enjoy moving between algorithms, runtimes, systems, and hardware rather than staying inside a single abstraction.\n\nCreated a technical artifact such as a paper, design document, blog post, demo, or talk that clearly explains what you built and what you learned.\n\nMeet Inferact at Waterloo\nInterested in learning more about Inferact, vLLM, and the engineering problems you could work on during your co-op? Join us for our University of Waterloo Employer Information Session to meet the team, learn more about Inferact, and hear about the Inference Engineering Co-op opportunity.\nVenue: Tatham Centre, Room 2218\n\nDate: Monday, October 5, 2026\n\nTime: 5:00 PM - 7:00 PM ET\n\nLogistics\nLocation: San Francisco, California. This co-op is in-office only at Inferact's San Francisco office and is intended for the University of Waterloo co-op program.\n\nWork term: Co-op / internship. Exact dates will align with the applicable Waterloo work term.\n\nCompensation: Competitive compensation based on the applicable co-op market and candidate background, plus a housing stipend for the duration of the co-op term.","description_format":"text","description_chars":6690,"description_truncated":false,"requirements":{"experience_years_min":null,"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":false,"industries":["AI Compute & Inference","AI Development Tools"],"lifecycle":[{"event":"open","at":"2026-09-11T03:00:30Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 5","filings_12m":5,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":245500,"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)"],"filings_for_role_12m":5}],"liveness":{"score":14,"band":"cold","label":"Long shot","p_open":1,"p_active":0.313,"p_room":0.45,"age_days":52,"expected_fill_days":28,"reasons":["conf:0","stale_co","velocity","win:tail"],"computed_at":"2026-11-02T06:13:15Z"},"pay":null,"html_url":"https://alion.io/job/inferact-inference-engineering-co-op","json_url":"https://alion.io/job/inferact-inference-engineering-co-op.json","meta":{"generated_at":"2026-11-02T23:49:07Z","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":33343,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-03T00:00:00Z"}},"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":678585},"rest":"https://alion.io/mcp/rest/get_company?id=678585"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Finferact-inference-engineering-co-op"},{"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%2Finferact-inference-engineering-co-op"},{"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%2Finferact-inference-engineering-co-op"},{"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/inferact-inference-engineering-co-op\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Finferact-inference-engineering-co-op"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":701844}},{"tool":"search_jobs","arguments":{"company":"inferact.ai","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":701844},"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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