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Build a career powered by innovations that matter! At Novanta, our innovations power technology products that are transforming healthcare and advanced manufacturing—improving productivity, enhancing people’s lives and redefining what’s possible. We create for our global customers engineered components and sub\-systems that deliver extreme precision and performance for a range of mission\-critical applications—from minimally invasive surgery to robotics to 3D metal printing.
Novanta is one global team with over 26 offices located in The Americas, Europe and Asia\-Pacific. Looking for a great place to work? You have found it with a culture that embraces teamwork, collaboration and empowerment. Come explore Novanta.
Job DescriptionAI Strategy Director – Remote (US)
Lead Enterprise AI Strategy at Novanta
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Novanta is seeking an experienced AI Strategy Director to define and lead our enterprise AI vision. Reporting to the CIO, you will set strategic priorities, identify high\-value AI opportunities, and ensure initiatives deliver measurable business impact across our global organization.
What you will do
- Develop and own Novanta’s AI strategy aligned with business objectives.
- Identify and prioritize AI use cases with clear success metrics.
- Evaluate emerging AI technologies and foundation models for enterprise adoption.
- Partner with data science, engineering, and IT teams to deliver scalable AI solutions.
- Oversee governance, compliance, and ethical considerations for AI initiatives.
- Define infrastructure and resource requirements for AI development and deployment.
- Establish frameworks for model lifecycle management, performance monitoring, and risk mitigation.
- Advise senior leadership on AI trends, regulatory changes, and competitive positioning.
What you will bring
- 8\+ years in technology strategy or IT leadership, including 3\+ years in AI/ML.
- Proven track record of shaping and executing AI strategies in complex environments.
- Strong understanding of AI architectures, data pipelines, and governance frameworks.
- Ability to communicate strategic and technical concepts to executive stakeholders.
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
- Experience leading cross\-functional teams and managing external partnerships.
- Familiarity with regulatory, ethical, and risk considerations in AI adoption.
Why Novanta?
We build precision technologies that power innovation in healthcare and advanced manufacturing. Our AI initiatives support mission\-critical applications—from surgical robotics to industrial automation. You’ll join a global team, work remotely, and help define how AI shapes real\-world outcomes.
Benefits
The salary for this role will range from $160,400 to $256,600 annually based on full\-time employment. Salary offers are based on a wide range of factors including but not limited to location, relevant skills, training, experience, education, etc.
Certain roles may be eligible for performance\-based incentive compensation and/or long\-term incentives. Incentives could be discretionary or non\-discretionary depending on the plan.
Novanta supports all aspects of your life’s needs. This position provides a full range of medical, financial, and other benefits to make your quality of life better.
Novanta is proud to be an equal employment opportunity and affirmative action workplace. We consider all qualified applicants without regard to race, color, religion, sex (including pregnancy), sexual orientation, gender identity or expression, national origin, military and veteran status, disability, genetics, or any other category protected by federal law or Novanta policy.
Please call \+1 781\-266\-5700 if you need a disability accommodation for any part of the employment process.
Salary Context
This $160K-$256K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Novanta, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($208K) sits 5% below the category median. Disclosed range: $160K to $256K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Novanta AI Hiring
Novanta has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $256K - $256K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 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.
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.
Frequently Asked Questions
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