{"id":2555406,"url":"https://alion.io/job/accellor-gen-ai-engineers-claude-code-codex-cursor","title":"Gen AI Engineers (Claude code, Codex, Cursor)","company":{"id":456,"name":"Accellor","domain":"accellor.com","url":"https://alion.io/company/accellor","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":91,"open_postings":25,"ghost_share":0,"stale_share":0.16,"repost_share":0,"time_to_fill_p50_days":65,"computed_at":"2026-10-15T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":161000,"max_usd":352000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ChatGPT","optional":false},{"name":"Claude Code","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"JAX","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Transformers","optional":false},{"name":"Triton","optional":false},{"name":"C++","optional":true},{"name":"LLM","optional":true},{"name":"Multimodal AI","optional":true},{"name":"NCCL","optional":true},{"name":"ONNX","optional":true},{"name":"Quantization","optional":true},{"name":"RAG","optional":true},{"name":"TensorRT","optional":true},{"name":"XLA","optional":true}],"status":"live","first_seen_at":"2026-10-14T19:02:06Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-15T06:44:52Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value.\nOur offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability.\nWith a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation.\nRole Summary:\nWe are looking for Gen AI Engineer with strong problem-solving ability, exceptional Claude code, codex expert, cursor, solid Python engineering skills, and hands-on understanding of machine learning, deep learning, GPUs, and CUDA fundamentals. The ideal candidate may be early in their career but must be technically sharp, curious, implementation-driven, and capable of learning complex AI systems quickly.\nThis role is suited for someone who can build models, debug training issues, optimize GPU workloads, understand tensor operations, and work closely with research, platform, and engineering teams.\nKey Responsibilities:\nDesign, build, train, and evaluate AI/ML models using Python, TensorFlow, PyTorch, or JAX.\nSolid skills with Claude code, codex, Cursor\nCan do large number of PRs per month\nDevelop clean training pipelines with data loading, checkpointing, logging, validation, and experiment tracking.\nWork on deep learning models including CNNs, Transformers, LLMs, embeddings, and attention-based architectures.\nSolid builder with architect mindset\nDebug model issues such as poor convergence, overfitting, unstable loss, NaNs, tensor-shape errors, and GPU memory failures.\nRun and optimize models on GPUs with awareness of CUDA execution, memory usage, batching, mixed precision, and kernel performance.\nProfile training and inference workloads to identify bottlenecks in compute, memory, data loading, and communication.\nSupport development of custom operators, fused kernels, CUDA/Triton-based optimizations, or framework-level performance improvements.\nBuild AI inference pipelines with focus on latency, throughput, reliability, cost, and quality.\nCreate evaluation pipelines for model accuracy, robustness, hallucination risk, safety, latency, and regression testing.\nRead research papers, implement ideas, run experiments, and clearly document findings, trade-offs, and limitations.\nRequirements\nStrong programming experience in Python.\nGood understanding of data structures, algorithms, numerical programming, and clean code practices.\nHands-on experience with TensorFlow, PyTorch, or JAX.\nStrong fundamentals in:\no Deep learning\no Backpropagation\no Gradient descent\no Optimizers\no Loss functions\no Regularization\no CNNs\no Transformers\no Attention mechanisms\no Embeddings\no Model evaluation\nExperience training and debugging models on CUDA-enabled GPUs.\nBasic understanding of:\no GPU memory\no CUDA kernels\no Threads, blocks, and warps\no Memory bandwidth\no Kernel launch overhead\no Mixed precision training\no CUDA out-of-memory debugging\nComfortable with Linux, Git, Docker, shell scripting, and experiment management.\nAbility to reason from first principles and solve ambiguous technical problems.\nPreferred Skills:\nCUDA C/C++ or Triton kernel development.\nC++ exposure for performance-critical AI systems.\nDistributed training using PyTorch DDP, TensorFlow Distributed Strategy, JAX, NCCL, MPI, or similar tools.\nExperience with GPU profiling tools such as:\no NVIDIA Nsight Systems\no NVIDIA Nsight Compute\no PyTorch Profiler\no TensorFlow Profiler\nExperience with inference optimization, quantization, TensorRT, ONNX, XLA, or model compilation.\nExposure to LLMs, RAG systems, agents, embeddings, multimodal AI, or AI evaluation frameworks.\nUnderstanding of FP32, FP16, BF16, INT8, tensor cores, and numerical stability.\nWhat Excellent Looks Like:\nCan implement a model from a research paper without blindly copying code.\nCan debug why a model is not learning.\nCan explain why GPU utilization is low.\nCan identify whether a workload is compute-bound, memory-bound, or communication-bound.\nCan write clean, reproducible, and testable Python code.\nCan profile before optimizing.\nCan work across model logic, GPU execution, data pipelines, and inference systems.\nCan communicate technical findings clearly and precisely.\n\n Ideal Candidate Profile:\nThe ideal candidate is an early-career AI engineer who is not limited to using ML libraries as black boxes. \nThey understand how models train, how tensors move, how GPUs execute workloads, and how performance, quality, reliability, and safety come together in real AI systems.\nThe strongest candidates will show hands-on projects in model implementation, GPU acceleration, CUDA/Triton operators, distributed training, LLM evaluation, inference optimization, or research paper reproduction.","description_format":"text","description_chars":5358,"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":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","AI Agents","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-10-14T19:02:06Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 4","filings_12m":4,"filings_prev_12m":5,"green_card_filings_12m":0,"median_offered_wage_usd":126250,"route":null,"cap_exempt":false,"checked_at":"2026-10-03T21:08:04+00:00","sources":["US Department of Labor: 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