Interested in this AI/ML Engineer role at Opentrons Labworks?
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About This Role
Job Title: Director of AIDepartment: Software*FLSA Classification:* *Exempt*Reports to: VP of Engineering
Direct Reports: Yes
Location: Onsite at Innolabs \- Long Island City, NY
- *Must reside in a commutable distance to NYC*
About Us
Opentrons Labworks, Inc. is a disruptive life science company leveraging its integrated lab platform to supercharge the pace of innovation in research and healthcare. Through Opentrons Robotics, thousands of institutions are automating lab operations with flexible, easy\-to\-use liquid handling lab robots. With our own cutting\-edge R\&D team, biopharma and biotech at large can also benefit from our world\-class genome\-scale cell engineering solutions.
If you are looking for your next great role, come join us! We provide excellent benefits, competitive compensation, equity packages, and opportunities for growth. Learn more on our Careers Website. About the RoleWe are seeking a Director of AI to lead the next phase of OpentronsAI strategy and platform capabilities. This role will focus on advancing AI\-powered experiences across our robotics and software ecosystem, with particular emphasis on generative AI, intelligent workflow design, and production\-grade AI systems for laboratory automation.
The role operates at the intersection of AI research, product strategy, and engineering execution, translating emerging AI capabilities into reliable tools used by scientists in real\-world laboratory environments. This is a highly cross\-functional role centered on technical vision, roadmap development, hands\-on collaboration, and long\-term platform strategy. The right candidate will not only set the direction but also expect to roll up their sleeves and execute work personally.
The Director of AI will report into the VP of Engineering. What You Would DoLead AI Product \& Platform Strategy:* Define and execute Opentrons’ AI roadmap in partnership with software, product management, and engineering leadership
- Drive the evolution of AI\-powered workflow and protocol generation capabilities across the Opentrons platform
- Balance near\-term product delivery with longer\-term research and platform investments
- Identify opportunities to apply generative AI, ML infrastructure, and intelligent automation to improve the scientist experience
Build Production\-Grade AI Systems:* Lead development of reliable, scalable AI systems that translate natural language inputs into validated laboratory workflows and protocols
- Establish best practices for evaluation, validation, trust, and performance of AI systems in production environments
- Partner closely with engineering teams to operationalize AI capabilities across software and robotics products
- Help shape the technical architecture and infrastructure required to support future AI initiatives
Drive External Innovation \& Thought Leadership:* Build strategic relationships with academic institutions, industry partners, and the broader AI ecosystem
- Support collaborative research, technical partnerships, and recruiting pipelines for top AI talent
- Represent Opentrons externally through conferences, publications, panels, and industry engagement
- Help position Opentrons as a leader in AI\-enabled laboratory automation and intelligent scientific systems
Management* Lead \& work with a small team of engineers and developers to build Opentrons’ AI\- and ML\-based product portfolio
- Structure processes and interaction models with other departments and stakeholders
- Build and expand the team as needed and funded by company goals
Who We Are Looking ForExperience:* Proven professional experience in artificial intelligence, machine learning, or applied research and engineering environments
- Experience designing AI systems with a strong focus on reliability, validation, usability, and operational performance
- Proven track record of delivering AI\-powered products from concept through production deployment and ongoing iteration
- Experience working across multidisciplinary organizations spanning software, hardware, and product development
Knowledge, Skills, \& Abilities:* Strong expertise in generative AI, foundation models, and modern AI/ML infrastructure
- Deep familiarity with Python\-based AI development ecosystems and production AI workflows
- Ability to define technical strategy and execute effectively in fast\-moving, resource\-constrained environments
- Strong communication and collaboration skills, including the ability to explain complex technical concepts to varied audiences
- Comfortable operating as both a strategic leader and hands\-on technical contributor
Nice to Have:* Experience within life sciences, laboratory automation, robotics, or scientific software platforms
- Experience collaborating with academic research institutions or contributing to published research
- Prior experience building AI\-enabled developer tools, workflow systems, or no\-code platforms
Working Conditions and Physical Effort* Work is performed in a typical interior/office work environment
- Prolonged periods of sitting at a desk and working on a computer
- Limited physical effort required
Compensation Range:The pay range for this position at commencement of employment is expected to be between $265,000 and $295,000 per year. Base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and years of experience. We typically target mid\-range for well\-qualified candidates. The total compensation package for this position may also include other elements, including bonus, equity and full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. Following employment, growth beyond the hiring range is possible based on performance. *Opentrons Labworks Inc. is an equal opportunity employer and does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non\-merit factor.* *The position will be posted until a final candidate is selected for the requisition or the requisition has a sufficient number of applications.*
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Salary Context
This $265K-$295K range is above the 75th percentile 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 Opentrons Labworks, 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 $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 ($280K) sits 28% above the category median. Disclosed range: $265K to $295K.
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.
Opentrons Labworks AI Hiring
Opentrons Labworks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Long Island City, NY, US. Compensation range: $295K - $295K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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