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About This Role
Senior Manager, AI/Machine Learning & Sensors
The Elevator Pitch Join Evolv as Senior Manager of AI/Machine Learning & Sensors for Expedite, our X-ray screening product. You will lead a multidisciplinary team of scientists and engineers developing state-of-the-art computer vision and deep learning solutions for X-ray imaging that operate in real-time on constrained hardware platforms. This role blends deep technical expertise—on par with our most senior individual contributors—with strong people leadership, stakeholder management, and project management skills. You will own delivery of the Expedite ML roadmap end-to-end: setting technical direction, running execution, developing your team, and representing the program in stakeholder and executive forums. Your leadership will push the boundaries of AI-powered physical security screening and ensure Evolv remains at the forefront of technological advancements in the industry.
Success in the Role: What are performance outcomes over the first 6-12 months you will work toward completing?In the first 30 days, you will:
- Explore Evolv's sensors, algorithms, and development processes, with a focus on the Expedite X-ray product architecture and data pipeline.
- Meet 1:1 with every member of the Expedite team of scientists and engineers to understand strengths, development goals, and current workstreams.
- Work to understand real-time processing, hardware limitations, and environmental constraints for X-ray screening in the Evolv product portfolio.
- Review the existing Expedite roadmap, model performance metrics, and delivery processes, and identify areas for improvement and deliver key milestones
- Build a stakeholder map across product, hardware, software engineering, regulatory, and customer-facing teams.
Within the First Three Months:
- Establish a predictable execution cadence for the Expedite team, including sprint planning, milestone tracking, and risk management.
- Own and run recurring stakeholder meetings, providing clear status, trade-offs, and decision recommendations to product and executive stakeholders.
- Work with the product team to gather requirements and translate these into derived requirements that guide and inform ML development efforts.
- Provide hands-on technical guidance on X-ray computer vision and deep learning approaches, including model architecture and evaluation decisions.
- Assess team structure and skills against the roadmap and propose a hiring and development plan.
By the End of the First Year:
- Deliver the major Expedite ML roadmap milestones on schedule, with production models deployed at the edge on resource-constrained hardware.
- Build a high-performing, engaged team of scientists and engineers with clear career development paths, and successfully hire to plan.
- Effectively communicate with senior leadership to provide insights that shape strategic decisions for the Expedite product line.
- Execute the company's X-ray and sensor-driven AI strategy, aligning it with business objectives to drive growth and innovation.
The Work: What type of work will you be doing? What assignments, requirements, or skills will you be performing on a regular basis?People Leadership & Team Management:
- Manage, mentor, and grow a team of ML scientists and engineers working on the Expedite X-ray product.
- Set clear goals and expectations, run regular 1:1s, deliver feedback, and own performance and career development for the team.
- Recruit, interview, and onboard top ML and engineering talent.
- Drive an innovation culture while maintaining a practical, delivery-oriented focus.
Project & Stakeholder Management:
- Own project planning and execution for Expedite ML workstreams: scoping, scheduling, resourcing, risk management, and delivery.
- Prepare and lead stakeholder meetings with product, hardware, software engineering, regulatory, and executive audiences, communicating status, trade-offs, and recommendations clearly.
- Coordinate cross-functional dependencies with product, hardware, software engineering, and SQA to integrate ML solutions into the Expedite platform.
- Enable rapid development through prioritization, planning, and automation.
- Engage in customer demonstrations and proof-of-concept exercises to quickly gather feedback and incorporate it into an iterative design and development process.
Technical Leadership:
- Set technical direction for X-ray computer vision and modern machine learning initiatives, including detection architectures and advanced AI/ML infrastructure and models to scale to current and future business use cases.
- Guide the design of novel algorithms that leverage X-ray imagery, including approaches that combine and fuse multiple sensor inputs.
- Recommend and implement best practices in model training, validation, and deployment for production edge systems.
- Review technical designs and results, and stay hands-on enough to unblock the team and pressure-test key decisions.
- Stay current with the latest ML developments and drive continuous improvement in X-ray sensing capabilities.
- Inform AI and sensor initiatives at the executive level.
Technical Expertise Required: Machine Learning & Computer Vision
- PhD in Computer Science, Machine Learning, Physics, or related field with 5+ years' experience in computer vision/deep learning, or MS with 10+ years' experience.
- 3+ years of direct people management experience leading teams of scientists and/or engineers.
- Proven experience delivering scalable production models, ideally on resource-constrained edge hardware.
- Strong command of emerging tools and architectures in machine learning.
- Experience with X-ray imaging, radiographic or other penetrating-sensor data (or closely related sensing modalities) strongly preferred.
- Expertise in sensor fusion algorithms and multi-sensor systems.
- Experience with TensorFlow, PyTorch, and other ML frameworks; strong programming skills in Python and C++.
- Knowledge of hardware acceleration technologies (GPU, NPU and TPU).
- Demonstrated project management skills (agile planning, milestone tracking, risk management) and excellent written and verbal communication for stakeholder and executive audiences.
What is the leadership like for this role? What is the structure and culture of the team? You will join our R&D organization, reporting directly to the VP of ML and Sensors, and will directly manage the Expedite team of scientists and engineers. In this role, you will interface with cross-disciplinary teams of highly skilled and autonomous engineers with expertise in Electromagnetics, Computer Vision, and AI. Our R&D organization includes more than 100 dedicated developers, engineers, and managers—each bringing deep technical knowledge and a strong culture of collaboration and support.
The team culture is one based on building trust, collaboration, on-going development through kindness, authenticity, courage, drive, and fun!
Where is the role located? This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of our software-enabled hardware products, this position requires a minimum of 60% on-site work (3-days a week).
Compensation and Transparency Statement: The base salary range for this full-time position is $187,000-$243,000. In addition to base salary, this role offers a competitive target bonus, equity, and a comprehensive benefits package. This range reflects our commitment to pay transparency and equity, in alignment with applicable state laws. Our compensation ranges are determined based on factors such as role, level, location, market benchmarks, and internal equity. The posted range represents the good-faith estimate of what we expect to pay for this role across U.S. locations. Actual compensation within the range will be based on the candidate’s skills, experience, education, and geographic location.
In accordance with state and local pay transparency laws—including those in California, Colorado, Massachusetts, New York, New Jersey, and others—we disclose salary ranges in all job postings and provide additional information upon request.
During the hiring process, your recruiter will share:
- The specific salary range for your preferred location
- A general overview of our benefits and equity offerings
- Insights into how compensation decisions are made, including factors that influence starting pay
We are committed to fair pay practices, and we regularly review our compensation programs to ensure they are competitive, equitable, and aligned with our values.
Benefits
At Evolv, we’re on a mission to help make public spaces safer through innovative security technology. So, we're looking for future teammates who embody our values, people who:
- Do the right thing, always;
- Put people first'
- Own it;
- Win together; and continue to
- Be bold, stay curious.
Our Benefits Include:
- Equity as part of your total compensation package
- Medical, dental, and vision insurance
- Health Savings Account (HSA)
- A 401(k) plan (and 2% company match)
- Flexible Paid Time Off (PTO)- take the time you need to recharge, with manager approval and business needs in mind
- Quarterly stipend for perks and benefits that matter most to you
- Tuition reimbursement to support your ongoing learning and development
- Subscription to Calm
Evolv Technology (“Evolv”) is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We welcome and encourage diversity in the workplace, and all employment decisions are made without regard to race, color, religion, national, social or ethnic origin, sex (including pregnancy), age, disability, HIV Status, sexual orientation, gender identity and/or expression, veteran status, or any other status protected by law in the locations where we operate. Evolv will not tolerate discrimination or harassment based on any of these characteristics.
Evolv is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation as part of the job application process, please connect with us at [email protected].
Evolv participates in E-verify for all employees after the completion of Form I-9.
Salary Context
This $187K-$243K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Evolv Technologies, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $187K to $243K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Evolv Technologies AI Hiring
Evolv Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US. Compensation range: $243K - $243K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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