BV Teck is actively hiring for 40 AI and machine learning positions across AI/ML Engineer (30), MLOps Engineer (2), and LLM Engineer (2) roles. Posted salary ranges span $150K - $175K, with 100% of listings disclosing compensation. The median posted ceiling sits at $150K. The majority of these positions (92%) are listed as remote, with physical offices in Tempe, AZ, US, Hoboken, NJ, US, Remote, US. The most frequently requested skills across these postings are Python, Pytorch, Kubernetes, Rlhf, Jax. Mid-level roles account for 97% of openings.
Skills & Technologies
Locations
Tempe, AZ, US, Hoboken, NJ, US, Remote, US
Hiring by Role Category
Open Positions (showing 25 of 40)
ML Performance Engineer
ML Platform Engineer
AI Pipeline Engineer
AI Learning Systems Engineer
Large Language Model Specialist
AI Cybersecurity Engineer
MLOps Engineer
ML Infrastructure Engineer
Machine Learning Research Engineer
Machine Learning Data Engineer
Machine Learning Infrastructure Engineer
AI Scientist
AI Optimization Engineer
AI Prompt Designer
Generative AI Specialist
LLM Prompt Specialist
ML Systems Engineer
AI Interaction Designer
Senior Data Scientist
Machine Learning Engineer – RL
AI Operations Engineer
Secure AI Systems Engineer
Research Scientist – AI
AI Platform Engineer
AI Applications Engineer
What BV Teck's hiring tells you
With 40 active AI roles spanning 8 role types, hiring at this scale signals AI is core to the business model, not a pilot. Companies in this tier typically have a named AI leader (VP AI, Head of ML), dedicated infrastructure budget, and a multi-year roadmap. Posted compensation range ($150K - $175K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in AI/ML Engineer roles. That is a clue about what BV Teck is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the BV Teck interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- How is the AI org structured, and who does it report to (CTO, CEO, separate AI leader)?
- What was the most recent ML system that shipped to production, and what was the scope?
- How much of compute spend is on inference vs training, and how is that decided?
BV Teck AI and ML Hiring
BV Teck has 40 active AI and ML roles in our dataset. Open positions span AI/ML Engineer, MLOps Engineer, LLM Engineer, Research Engineer. Compensation ranges from $150K - $175K across disclosed roles. Roles are based in Tempe, AZ, US, Hoboken, NJ, US, Remote, US.
Salary Benchmarks
The market median for AI roles is $217,500. AI/ML Engineer roles pay a median of $218,750 across the market. MLOps Engineer roles pay a median of $220,000 across the market. LLM Engineer roles pay a median of $203,940 across the market. Top-quartile AI compensation starts at $272,100.
Skills BV Teck Looks For
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.
AI Role Categories
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $218,750 median across 3,817 positions with disclosed pay.
MLOps Engineer
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
Market compensation for MLOps Engineer roles: $220,000 median across 47 positions with disclosed pay.
LLM Engineer
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Market compensation for LLM Engineer roles: $203,940 median across 9 positions with disclosed pay.
Research Engineer
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Market compensation for Research Engineer roles: $280,000 median across 147 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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.
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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