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About This Role
Senior AI Solutions Engineer
Location: Hybrid\- Boston (2 days a week in our Boston Office)
Status: Permanent
Package: Competitive Salary, Remote/Home Working (with one\-off allowance), Flexible Working, (2 Days in the office for our Hybrid workers) Development \& Opportunity (Personal \& Technical), Medical Insurance, Dental Insurance, Vision Insurance, Life Insurance, Long Term \& Short\-Term Disability Insurance, Generous 401K (matching) Plan, Flexible Spending Account, Health Savings Account, 15 Days' Vacation \+ Plus Public Holidays \+ Buy and Sell Scheme.
At Instem, you’ll empower pharmaceutical companies, CROs, government agencies, academic institutions, by providing cutting\-edge SaaS solutions and consultancy services designed to help researchers make discoveries, and to accelerate and advance their research programs.
You’ll work with technology that supports over 50% of global drug discovery and is trusted by leading pharmaceutical, biotech and CRO organisations worldwide.
This is a senior individual contributor role focused on realising AI value across Instem — identifying enterprise opportunities, delivering quick wins, onboarding departments to agentic ways of working, and surfacing opportunities for AI inside the products we sell to customers.
You will work directly with business functions — commercial, operations, quality, finance, HR, support — to find where agents can remove friction, automate process, and unlock new capability. You'll then build, deploy, and embed those solutions, agentically where possible.
This is a hands\-on role: you will discover opportunities, prototype rapidly, deliver measurable outcomes, and enable teams to continue the work themselves. Think part solutions engineer, part internal consultant, part builder.
Our Culture \& Work Environment
You’ll be part of a friendly, solutions\-focused environment within a global business of over 400 people. Here, you’ll experience a high\-performance culture balanced with genuine flexibility \- we trust you to take ownership, deliver meaningful results, and work in a way that suits you.
You’ll be encouraged to grow, share your ideas, and make a real impact. We’re passionate about creating a place where you can develop your skills, contribute confidently, and succeed in your career.
Why join us as a Senior AI Solutions Engineer?
This role is brand new and one of a kind!
Your work operates across three AI maturity levels, applied progressively to each business function. Everyday AI is AI that makes daily work faster: off\-the\-shelf tools with brand\-trained prompts and Claude Skills, heavy human in the loop, individuals own and refine their own prompts, time to ship is days. Examples include image and document generation, slide creation, translation and regionalisation, data cleaning and formatting, and internal communication assembly. Functional AI is AI embedded into function workflows: custom agents built by a small AI power\-user core inside each function in close collaboration with IT and engineering, decisions are systematised, humans review at gates, time to ship is weeks. Examples include signal\-routing agents, prospecting and lead\-qualification agents, brand and content review agents, quoting and proposal\-support agents, and segmentation enrichment agents. Enterprise AI is AI connecting workflows across functions: end\-to\-end orchestrated workflows on top of a shared knowledge layer with agent ops, evaluation, and observability infrastructure, time to ship is months. Examples include knowledge graphs, modular content registries, citation and claim provenance, cross\-system attribution, and talk\-to\-data over the enterprise data estate.
You will partner with the AI Platform and Product Engineering functions — consuming their shared capabilities, feeding back real\-world requirements, and helping set the direction for how Instem operates with AI, both internally and in our products.
You will frequently be the Instem\-side technical owner of external AI partner engagements. Each major business function may bring in an external AI partner for a Phase 1 build (marketing is the first; sales operations, customer success, solutions and implementation consulting, outsourced services, finance, and people and culture will follow in sequence set with the Chief Scientific Officer).
What You’ll Be Doing
- Foundation Tracks (gating prerequisites for every downstream pilot)
- Current\-state audit of the tools and platforms in each business function in scope (CRM and marketing automation in commercial; support and ticketing systems in client support; LMS and training systems in education services; finance and HR systems in their respective functions; document management, internal knowledge bases, and data warehouses everywhere). AI inventory; data quality and attribution baseline. Output gates every downstream pilot.
- Shared knowledge layer: brand corpus, product knowledge, scientific claims library, modular content registry, customer data. Centralised, versioned, Instem\-owned. The single asset every agent reads from and writes to.
- Evaluation and observability infrastructure: deterministic evals, LLM\-as\-judge harnesses, production logging, edit\-distance and task\-success tracking. No prompt or agent reaches production without an eval that holds.
Opportunity Discovery \& Enablement
- Partner with department leaders to map current processes and identify high\-value AI opportunities
- Run lightweight discovery sessions that surface quick wins and larger transformation plays
- Prioritise opportunities by value, effort, risk, and readiness — and help the business sequence them
Quick Wins \& Solution Delivery
- Design, build, and deploy agentic solutions (Claude\-based where appropriate) that deliver measurable outcomes within weeks, not quarters
- Build using the existing agentic ecosystem — Cowork, Claude Code, MCP servers, skills, plugins, and platform\-provided components — extending only when necessary
- Ship end\-to\-end: from prompt and skill authoring, through tool integration, to rollout and handover
External Partner Engagement \& Capability Transfer
- Act as Instem\-side technical owner of external AI partner engagements as each major business function brings one in (marketing first; sales operations, customer success, solutions and implementation consulting, outsourced services, finance, and people and culture in sequence)
- Receive each vendor handoff: code, prompts, evals, repository, runbooks. Operate vendor\-free within 30 days. Document gaps and resolve them.
- Stand up small AI power\-user cores inside each function: colleagues trained to prompt and build agents themselves with your support
- Transition external partner work to internal build cycles led by you as each partner exits. Over time the proportion shifts: less time receiving handoffs, more time building from scratch.
Department Onboarding \& Adoption
- Lead AI onboarding for departments — from exec briefings, through hands\-on enablement, to embedded champions
- Produce adoption playbooks, templates, and patterns tailored to each function's workflow
- Track adoption, usage, and outcomes — and iterate based on what the data shows
Process Improvement \& Automation
- Improve and automate internal processes using agents — building where possible, buying where sensible
- Identify and remove the manual, repetitive, and low\-value steps that agents are well\-suited to absorb
- Partner with process and quality owners to ensure improvements are compliant and auditable
AI Opportunities in Our Products
- Partner with Product Management, Product Engineering, and customer\-facing teams to identify where AI can improve customer workflows, automate effort, and drive customer success inside Instem's products
- Act as a business\-analyst\-style function for AI in our products — spotting opportunities, framing business cases, capturing requirements, and defining success metrics
- Ideate and lightly prototype concepts to prove value and de\-risk direction before Product Engineering invests in full delivery
- Hand well\-scoped opportunities over to the Principal AI Product Engineer and wider Product Engineering teams, who lead build and production delivery
- Stay close through delivery to ensure intent is preserved, customer feedback is captured, and adoption is supported post\-launch
Ecosystem Contribution
- Feed requirements, patterns, and reusable building blocks back to the AI Platform and Product Engineering teams
- Contribute skills, prompts, MCP servers, and plugins to Instem's shared agentic ecosystem
- Share learnings, demos, and playbooks broadly — lifting AI capability across the business
Governance \& Safe Adoption
- Ensure solutions respect Instem's AI usage, data handling, and governance expectations
- Work within the Architecture engagement model for automations, agents, and embedded workflows
- Flag risk, confidentiality, and compliance questions early — and route them through the right channels
What We’re Looking For
- Significant experience delivering technology solutions in enterprise settings — solutions engineering, product engineering, internal tools, business analysis, or consulting backgrounds all welcome
- Hands\-on experience building with Claude (or comparable frontier models), including prompts, tools, and agent\-style workflows
- Practical experience with Instem's agentic surfaces — Cowork, Claude Code, MCP, skills, plugins — or demonstrable ability to ramp quickly
- Track record of identifying, scoping, and delivering process or automation improvements with measurable business outcomes
- Business\-analysis\-style skills — framing problems, writing clear requirements, defining success metrics, and handing work over well
- Experience partnering with Product Management and Product Engineering teams to move opportunities from idea into delivery
- Strong stakeholder management — able to work credibly with exec sponsors, process owners, product leaders, and hands\-on users
- Proficient in at least one of Python or TypeScript; comfortable integrating with SaaS APIs, spreadsheets, and enterprise systems
- Comfortable with web fundamentals, auth, scripting, and basic infra — enough to ship small production\-ready automations end\-to\-end
- Experience running discovery, prioritisation, and enablement sessions with non\-technical teams
- Awareness of security, data protection, and governance considerations in enterprise and customer\-facing AI deployments
- Ability to write clearly — playbooks, runbooks, adoption guides, opportunity briefs, and internal comms
- Bachelor's degree in a relevant discipline, or equivalent practical experience
Desirable:
- Experience in regulated environments (life sciences, healthtech, GxP) or working with QMS\-aligned processes
- Experience authoring MCP servers, Claude skills, or Cowork plugins for internal use
- Background in enterprise change management, adoption, or internal product roles
- Experience in a product\-facing BA, product owner, or solution consultant capacity — translating customer need into shippable product requirements
- Familiarity with Microsoft 365 Copilot, agents, Power Platform, or comparable enterprise AI tooling
- Experience working alongside Architecture or governance functions on AI\-embedded workflows
- Knowledge of Instem's customer base and domain (preclinical, regulatory, life sciences) sufficient to spot where AI adds customer value
What We Offer
We invest in you because you’re at the heart of our success. Depending on your location, you’ll have access to a range of benefits, including:
- Comprehensive Healthcare (Medical, dental, vision, life, and more)
- Comprehensive wellbeing and support initiatives
- Clear career pathways to support you with getting to the next level
- Pro\-rated and immediate vacation days
- Continuous training and development
- Generous retirement plans
Ready to Make a Difference?
Join a team that values your perspective and supports your growth, while helping shape the future of life sciences. Apply now \- we’d love to hear from you, and our Talent team will be in touch.
An Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
*Instem stores and processes data using an Applicant Tracking System (ATS). For more information regarding our privacy policy use the following link: https://www.instem.com/privacy/*
*\#LI\-KL \#LI\-HYBRID*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Instem, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Instem AI Hiring
Instem has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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