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About This Role
At Locus Robotics, we build AI\-powered systems and intelligent robots that keep global supply chains running. Our platform combines advanced AI, real\-time decision\-making, and autonomous robotics to help leading companies improve efficiency, scale operations, and adapt to constant change.
Locus Robotics is a place to do meaningful work with real\-world impact, where your ideas move quickly from concept to deployment. We invest in our people, encourage continuous learning, and give you the opportunity to grow your career while building technology that is used every day at global scale.
The Technical Director, AI Enterprise Architect is a highly visible, hands\-on individual contributor (IC) role with a direct mandate from the executive leadership team. As a strategic and technical leader, you will serve as a force multiplier across the organization, partnering with senior stakeholders to identify high\-impact AI opportunities, translate complex business challenges into scalable AI\-native solutions, and drive execution from concept through adoption.
This is a rare opportunity to join one of the largest privately held robotics companies in the United States at the forefront of Physical AI. You will play a pivotal role in shaping and accelerating an enterprise\-wide AI transformation initiative with full CEO and Board\-level sponsorship. Working across business and technology functions, you will have significant influence over AI strategy, architecture, implementation, and measurable business outcomes—owning both the direction and the impact of the company's AI\-first vision.
This is both a strategy and build role \- you will define the roadmap, architect the platform, and lead execution.
Responsibilities
Enterprise AI Transformation Strategy: Define and drive the company\-wide AI roadmap, including prioritization frameworks, sequencing of initiatives, and executive alignment. Ensure a relentless focus on business outcomes rather than tool adoption.
AI\-Native Workflow Redesign: Partner with leaders across Sales, Customer Success, Finance, Operations, and Marketing to identify high\-leverage opportunities. Redesign processes from the ground up into AI\-native, automated workflows.
AI Systems \& Agentic Workflow Development: Design, build, and deploy production\-grade AI systems, including agentic workflows that automate end\-to\-end processes. Own the full lifecycle—from scoping through deployment, monitoring, and iteration.
LLM \& Data Integration Architecture: Architect scalable LLM\-powered systems, including retrieval\-augmented generation (RAG), unified context layers, and integration frameworks that connect enterprise data sources.
Data \& Platform Engineering: Design and implement robust data pipelines, integration layers, and shared infrastructure that enable reusable, enterprise\-wide AI capabilities. Ensure reliability, scalability, and accessibility across systems.
AI Governance, Security \& Standards: Establish frameworks for model governance, risk management, data access, and security. Define standards for tools, evaluation, and responsible AI usage.
Technical Leadership \& Culture Building: Drive AI adoption across the organization by mentoring leaders, establishing best practices, and fostering AI\-native ways of working.
Core Expertise
- Deep expertise in modern AI techniques, including transformer architectures, multimodal systems, and LLM application design. Strong understanding of: Fine\-tuning and adaptation (LoRA, PEFT, RLHF/DPO), RAG systems, embeddings, and tokenization and Prompt engineering and tool\-augmented agents
- Proven track record designing and operating production\-grade AI systems that deliver measurable business impact (e.g., efficiency, revenue growth, cost reduction, user experience).
- Experience embedding AI into core enterprise systems (CRM, ERP, knowledge systems, collaboration platforms) to enable end\-to\-end workflow transformation.
- Strong grounding in: Data engineering (ETL/ELT, pipelines, APIs), Data architecture (Lakehouse, storage systems), Metadata systems (catalogs, lineage), Governance, security, and compliance frameworks
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related technical field required; Master's or PhD preferred
- 5\+ years leading enterprise AI or digital transformation initiatives, with demonstrated ownership of strategy through execution
- 5\+ years of hands\-on experience in software engineering, data engineering, or AI/ML roles, with strong proficiency in Python and modern cloud platforms (AWS, Azure, or GCP)
- Proven experience designing and deploying production\-grade AI systems, including agentic workflows that automate end\-to\-end processes and drive measurable business outcomes
- Deep expertise in LLM\-based systems, including RAG architectures, prompt engineering, tool integration, and enterprise use of foundation models (e.g., GPT\-4, Claude, or equivalent)
- Strong foundation in data engineering and architecture, including ETL/ELT pipelines, APIs, Lakehouse environments (e.g., Databricks), and data quality/governance frameworks
- Experience building scalable AI platforms including: Shared services, connectors, and agent frameworks, eEvaluation and observability tooling and deployment and scaling infrastructure
- Ability to operate at both strategic and deeply technical levels\- prototyping, architecting, and delivering complex AI systems in production environments.
- Working knowledge of classical machine learning techniques (regression, classification, anomaly detection, time\-series forecasting) and when to apply them vs. LLM\-based approaches
- Demonstrated ability to translate business problems into scalable technical solutions, with strong business acumen and outcome\-driven thinking
- Excellent English communication and leadership skills, with the ability to engage both technical teams and executive stakeholders and drive cross\-functional alignment
*The expected base salary range for this role is $200,000 \- $300,000 annually, based on external market data, plus bonus and equity. Actual offers will depend on factors such as the candidate's experience, education, training, key or critical skills, geographic location, and current market and business conditions*
Additional Information
Locus Robotics is an equal opportunity employer.
Application Fraud Detection Notice: To help maintain a fair and secure hiring process, Locus Robotics may use AI\-assisted and other automated tools to detect suspected fraud, misrepresentation, or misuse of the application process. Hiring decisions are not made solely by automated means unless otherwise disclosed where required by law.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Additional Information
Locus Robotics is an equal opportunity employer.
Application Fraud Detection Notice: To help maintain a fair and secure hiring process, Locus Robotics may use AI\-assisted and other automated tools to detect suspected fraud, misrepresentation, or misuse of the application process. Hiring decisions are not made solely by automated means unless otherwise disclosed where required by law.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $200K-$300K 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 Locus Robotics, 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 ($250K) sits 14% above the category median. Disclosed range: $200K to $300K.
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.
Locus Robotics AI Hiring
Locus Robotics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $300K - $300K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national 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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