Interested in this AI/ML Engineer role at Fidus?
Apply Now →About This Role
Fidus is a global high\-tech design firm headquartered in Ottawa, with additional design centres in Kitchener\-Waterloo and San Jose. We specialize in leading\-edge electronic product development, with hardware, embedded software, FPGA/ASIC, and signal integrity teams working together to design and deliver next\-generation products for clients in emerging technology markets.
Reporting to our CEO, the Chief AI Officer will lead our AI transformation – own and drive our AI strategy, roadmap, and adoption.
What You’ll Be Doing:
1\. AI Strategy \& Roadmap
- Own and drive the company\-wide AI strategy, roadmap, and governance framework
- Define the multi\-year vision for AI adoption across service delivery, business development, and operations
- Lead prioritization of AI initiatives against revenue, margin, and competitive differentiation outcomes
- Report to the CEO on AI transformation progress; own the AI KPI scorecard
2\. AI\-Enabled Products \& Services
- Collaborate with CTO and Delivery Leaders to identify and build net\-new AI\-enabled service offerings that expand Fidus's addressable market — e.g., AI\-assisted SI/PI analysis, automated design review, embedded AI in customer product development
- Lead productization of Fidus's engineering expertise into scalable, repeatable AI\-augmented deliverables
- Partner with discipline leads (FPGA, SI/PI, Layout, Software, Hardware, ASIC) to embed AI tooling into delivery workflows
- Establish IP strategy around proprietary AI methods and tooling developed internally
3\. AI Adoption \& Change Management
- Drive measurable adoption of AI tools across all teams — engineering, operations, and business development
- Design and execute a structured change management program: enablement, training, and accountability
- Define and own adoption KPIs: cycle time reduction, AI\-assisted delivery percentage, proposal and BD efficiency
- Champion a culture of experimentation while maintaining rigorous quality standards in safety\-critical engineering environments
4\. Data Strategy \& Infrastructure (in partnership with IT)
- Partner with IT to define and evolve Fidus's data architecture, governance, and quality standards as the foundation for AI capability
- Collaborate with IT to build and maintain the data platform required to support AI model development, training, and deployment
- Partner with IT and Legal to ensure data practices meet compliance requirements including ITAR, export control, and client NDA obligations
- Work closely with IT and engineering leadership to instrument delivery processes for systematic data capture and to maintain data quality at the source
5\. Client\-Facing AI Leadership
- Serve as Fidus's external AI authority — engaging clients, strategic partners, and industry forums
- Advise clients on AI integration into their hardware, firmware, and systems product development programs
- Support channel and business development by articulating Fidus's AI differentiation to silicon vendors, EDA partners, and the broader partner ecosystem
6\. Build \& Lead the AI Organization
- Hire, develop, and manage the AI function — engineers, data scientists, and ML operations
- Define the organizational model: centralized Center of Excellence or embedded discipline\-level AI leads
- Manage AI vendor and tool relationships — model providers, MLOps platforms, and data tooling — in coordination with IT procurement and security standards
Requirements Who You Are
- Services industry experience: Proven track record of driving AI adoption and transformation inside a services or professional services organization — not a product company. You understand the utilization model, the people dynamics of a billable delivery environment, and how to drive change without disrupting client commitments.
- AI transformation leadership: 10\+ years in technology leadership; 3\+ years in a senior AI/ML leadership role with demonstrated organizational impact. You have taken AI from concept to deployed, production\-grade capability in an organization that did not start AI\-native.
- Deep data background: Hands\-on expertise in data architecture, data governance, and ML pipeline development. Skilled at working across functional boundaries — able to engage credibly with both engineers and IT leadership, not just manage upward.
- Product development experience: Demonstrated experience building AI\-enabled products or productized service offerings, from ideation through commercialization.
- Execution track record: History of executing on strategic roadmaps under ambiguity with measurable business outcomes — revenue, margin, efficiency. You ship.
- Executive communication: Able to translate AI strategy into board\-level narrative and into day\-to\-day engineering direction. Equally fluent in both registers.
Strong Preference
- Background in engineering services, EDA, semiconductor, or adjacent deep\-tech industries
- Familiarity with safety\-critical or regulated engineering environments: A\&D, medical, automotive
- Experience in ITAR or export\-controlled data environments
- Prior experience in a company undergoing strategic transformation — high\-growth, PE\-backed, or M\&A integration
- Hands\-on experience with LLMs, agentic AI frameworks, or AI applied to CAD/EDA and hardware design workflows
About Fidus \& Why Work Here
Since 2001, Fidus has completed over 4,000 projects for more than 400 customers across industries including Telecom/Datacom, Aerospace \& Defence, Consumer, Semiconductors, Industrial/Automotive Controls, and Medical. As a Premier Adaptive Computing Partner for AMD North America, we combine deep technical expertise with industry\-leading tools to solve complex design challenges.
Fidus designs high\-speed, high\-complexity electronic systems using emerging technologies, often well before they reach the broader market. Our teams work across FPGA/DSP, hardware, embedded software, verification, SI/PI, PCB layout, and mechanical/thermal design to deliver real products for our customers.
Our work spans space and satellite systems, next\-generation communications and networking, high\-performance compute and storage platforms, advanced video and imaging, and industrial, medical, and automotive systems. We are not tied to a single product or industry—our work follows the technology, not just one roadmap.
At Fidus, collaboration is at the core of how we work. We value openness, trust, and shared ownership, and we create an environment where people are encouraged to contribute ideas, learn from one another, and have a meaningful impact through the work they do.
If all of this excites you, has your attention, and you can’t wait to join our amazing team and work environment, then we want to hear from you!
Curious what it's actually like to work here? Take a look at our Working at Fidus page.
*Fidus has a commitment to ensure a fair and transparent recruitment process. Automated tools (including AI) support our initial screening of applicants against our job posting to identify candidates whose qualifications, experience, and skills align most closely with the position requirements. All further candidate assessments and final selection are conducted by our human recruitment team. AI does not make any final hiring decisions.*
*Thank you for your interest in Fidus. We welcome and encourage diverse candidates to apply. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Fidus is committed to creating a diverse environment and is proud to be an equal opportunity employer.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fidus, 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Fidus AI Hiring
Fidus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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