Interested in this AI/ML Engineer role at Accellor?
Apply Now →Skills & Technologies
About This Role
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.
Our 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.
With 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.
Role Summary:
We 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.
This 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.
Key Responsibilities:
- Design, build, train, and evaluate AI/ML models using Python, TensorFlow, PyTorch, or JAX.
- Solid skills with Claude code, codex, Cursor
- Can do large number of PRs per month
- Develop clean training pipelines with data loading, checkpointing, logging, validation, and experiment tracking.
- Work on deep learning models including CNNs, Transformers, LLMs, embeddings, and attention-based architectures.
- Solid builder with architect mindset
- Debug model issues such as poor convergence, overfitting, unstable loss, NaNs, tensor-shape errors, and GPU memory failures.
- Run and optimize models on GPUs with awareness of CUDA execution, memory usage, batching, mixed precision, and kernel performance.
- Profile training and inference workloads to identify bottlenecks in compute, memory, data loading, and communication.
- Support development of custom operators, fused kernels, CUDA/Triton-based optimizations, or framework-level performance improvements.
- Build AI inference pipelines with focus on latency, throughput, reliability, cost, and quality.
- Create evaluation pipelines for model accuracy, robustness, hallucination risk, safety, latency, and regression testing.
- Read research papers, implement ideas, run experiments, and clearly document findings, trade-offs, and limitations.
Requirements
- Strong programming experience in Python.
- Good understanding of data structures, algorithms, numerical programming, and clean code practices.
- Hands-on experience with TensorFlow, PyTorch, or JAX.
- Strong fundamentals in:
o Deep learning
o Backpropagation
o Gradient descent
o Optimizers
o Loss functions
o Regularization
o CNNs
o Transformers
o Attention mechanisms
o Embeddings
o Model evaluation
- Experience training and debugging models on CUDA-enabled GPUs.
- Basic understanding of:
o GPU memory
o CUDA kernels
o Threads, blocks, and warps
o Memory bandwidth
o Kernel launch overhead
o Mixed precision training
o CUDA out-of-memory debugging
- Comfortable with Linux, Git, Docker, shell scripting, and experiment management.
- Ability to reason from first principles and solve ambiguous technical problems.
Preferred Skills:
- CUDA C/C++ or Triton kernel development.
- C++ exposure for performance-critical AI systems.
- Distributed training using PyTorch DDP, TensorFlow Distributed Strategy, JAX, NCCL, MPI, or similar tools.
- Experience with GPU profiling tools such as:
o NVIDIA Nsight Systems
o NVIDIA Nsight Compute
o PyTorch Profiler
o 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.
What Excellent Looks Like:
- Can implement a model from a research paper without blindly copying code.
- Can debug why a model is not learning.
- Can explain why GPU utilization is low.
- Can identify whether a workload is compute-bound, memory-bound, or communication-bound.
- Can write clean, reproducible, and testable Python code.
- Can profile before optimizing.
- Can work across model logic, GPU execution, data pipelines, and inference systems.
- Can communicate technical findings clearly and precisely.
Ideal Candidate Profile:
- The ideal candidate is an early-career AI engineer who is not limited to using ML libraries as black boxes.
- They understand how models train, how tensors move, how GPUs execute workloads, and how performance, quality, reliability, and safety come together in real AI systems.
- The 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Accellor, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Accellor AI Hiring
Accellor has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span San Jose, CA, US, Hyderabad, Telangana, India.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.