Gen AI Engineers (Claude code, Codex, Cursor)

Accellor•San Jose, CA

About The Position

Accellor is an AI-native services firm focused on delivering measurable business outcomes through AI, data, and engineering capabilities. Its offerings include AI solutions, data services, enterprise applications, and product engineering for industries including healthcare, life sciences, telecom, retail, financial services, and technology. Accellor is seeking a technically sharp, implementation-driven Gen AI Engineer with strong problem-solving ability, expertise with Claude Code, Codex, and Cursor, solid Python engineering skills, and hands-on knowledge of machine learning, deep learning, GPUs, and CUDA fundamentals. The role suits an early-career engineer who can learn complex AI systems quickly and work across model development, GPU execution, data pipelines, and inference systems in collaboration with research, platform, and engineering teams. The engineer will build, train, evaluate, debug, and optimize AI/ML models and workloads. The position also involves reading and implementing research, profiling training and inference, developing evaluation pipelines, and documenting technical findings and trade-offs. Strong candidates can reason about model training and tensor operations, diagnose GPU performance issues, and contribute to reliable, efficient AI systems.

Requirements

Strong programming experience in Python.
Strong problem-solving ability and the ability to reason from first principles and solve ambiguous technical problems.
Good understanding of data structures, algorithms, numerical programming, and clean code practices.
Hands-on experience with TensorFlow, PyTorch, or JAX.
Strong fundamentals in deep learning, backpropagation, gradient descent, optimizers, loss functions, regularization, CNNs, Transformers, attention mechanisms, embeddings, and model evaluation.
Experience training and debugging models on CUDA-enabled GPUs.
Basic understanding of GPU memory, CUDA kernels, threads, blocks, warps, memory bandwidth, kernel launch overhead, mixed-precision training, and CUDA out-of-memory debugging.
Comfort with Linux, Git, Docker, shell scripting, and experiment management.
Ability to build models, debug training issues, optimize GPU workloads, understand tensor operations, and learn complex AI systems quickly.
Strong capability with Claude Code, Codex, and Cursor.

Nice To Haves

CUDA C/C++ or Triton kernel development.
C++ exposure for performance-critical AI systems.
Distributed training experience with PyTorch DDP, TensorFlow Distributed Strategy, JAX, NCCL, MPI, or similar tools.
Experience with GPU profiling tools such as NVIDIA Nsight Systems, NVIDIA Nsight Compute, PyTorch Profiler, or TensorFlow Profiler.
Experience with inference optimization, quantization, TensorRT, ONNX, XLA, or model compilation.
Exposure to LLMs, RAG systems, agents, embeddings, multimodal AI, or AI evaluation frameworks.
Understanding of FP32, FP16, BF16, INT8, tensor cores, and numerical stability.
Hands-on projects in model implementation, GPU acceleration, CUDA/Triton operators, distributed training, LLM evaluation, inference optimization, or research paper reproduction.

Responsibilities

Design, build, train, and evaluate AI/ML models using Python and frameworks such as TensorFlow, PyTorch, or JAX.
Use Claude Code, Codex, and Cursor; contribute a large number of pull requests per month.
Develop training pipelines with data loading, checkpointing, logging, validation, and experiment tracking.
Work with deep learning models such as CNNs, Transformers, LLMs, embeddings, and attention-based architectures.
Debug model issues including poor convergence, overfitting, unstable loss, NaNs, tensor-shape errors, and GPU memory failures.
Run and optimize models on GPUs, considering CUDA execution, memory use, batching, mixed precision, and kernel performance.
Profile training and inference workloads to identify compute, memory, data-loading, and communication bottlenecks.
Support custom operators, fused kernels, CUDA/Triton optimizations, and framework-level performance improvements.
Build AI inference pipelines focused on latency, throughput, reliability, cost, and quality.
Create evaluation pipelines for accuracy, robustness, hallucination risk, safety, latency, and regression testing.
Read research papers, implement ideas, run experiments, and document findings, trade-offs, and limitations.
Work across model logic, GPU execution, data pipelines, and inference systems, and communicate technical findings clearly.