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Job Description:
We have a new opportunity for a Senior Software Development Engineer to join our Engineering team. In this role you will be working within a collaborative team on the development of next generation non-contacting measurement technologies and processes for video extensometer products. You will lead the design, implementation, and optimization of advanced algorithms and systems and will have deep expertise across optical and imaging system design, and high‑performance, real‑time software implementation. The successful candidate will serve as both a technical authority and hands‑on contributor, working across algorithm development, computational performance optimization, and integration with imaging hardware. This role plays a critical part in delivering robust, production‑grade solutions used in demanding real‑time and high‑accuracy measurement applications.
Key Responsibilities
- Collaborate on the design and selection of optical systems, including lenses, cameras, illumination, and stereo configurations.
- Design, implement, and maintain image analysis techniques and algorithms
- Develop and refine correlation metrics, interpolation schemes and strain calculation methods.
- Contribute to camera calibration, stereo reconstruction, and geometric correction
- Understand and optimize performance tradeoffs across resolution, frame rate, depth of field, and signal‑to‑noise ratio.
- Work closely with hardware teams to ensure seamless integration between imaging systems and DIC software.
- Optimize software for real‑time or near real‑time execution, including low‑latency processing pipelines.
- Apply performance optimization techniques such as multi‑threading, SIMD/vectorization, GPU acceleration (e.g., CUDA/OpenCL), and memory‑efficient architectures.
- Profile and benchmark code to identify and eliminate computational bottlenecks.
- Balance accuracy, robustness, and execution speed for production deployment
Knowledge, Skills and Abilities Required:
- Degree in Mechanical Engineering, Computer Vision, Computer Science, Applied Mathematics, Physics, or a related field
- Extensive hands‑on experience with machine vision software development, including practical deployment on real imaging systems.
- Strong foundation in computer vision, numerical methods, optimization, and image processing.
- Proven experience developing high‑performance, production‑quality software for computationally intensive applications.
- Ability to prototype algorithms in Matlab and deploy production implementations in C++
- Demonstrated ability to optimize software for speed, memory efficiency, and robustness.
- Desired attributes: + Proven strong interpersonal, communication and organizational skills + Able to create, collaborate and work within team environments. + Innovative with strong problem-solving capabilities. + Self-starter showing front to back ownership and sense of urgency
Preferred:
- Real-time machine vision measurement systems in aerospace, automotive, biomechanics, materials testing, or similar domains.
- GPU acceleration and heterogeneous computing architectures.
- Familiarity with uncertainty quantification or metrology standards
- Experience delivering commercial or customer‑deployed scientific software products.
- Familiarity with material testing systems
Compensation Information:
This position has a starting salary range of $140,000 to $160,000 per year and is incentive eligible. Pay is determined by several factors, including a candidate’s experience, relevant skills, and qualifications
ITW is an equal opportunity employer. We value our colleagues’ unique perspectives, experiences and ideas and create workplaces where everyone can develop their careers and perform to their full potential.
As an equal employment opportunity employer, ITW is committed to equal employment opportunity and fair treatment for employees, beginning with the hiring process and continuing through all aspects of the employment relationship.
All qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, national origin, age, disability, protected Veteran status or any other characteristic protected by applicable federal, state, or local laws.
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Instron, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Instron AI Hiring
Instron has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Norwood, MA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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