ML Engineer– Drug Discovery & R&D Operations

Indianapolis, IN, US Mid Level AI/ML Engineer

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Skills & Technologies

PythonPytorchTensorflowTransformers

About This Role

AI job market dashboard showing open roles by category

At Elanco (NYSE: ELAN) – it all starts with animals!

As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets. At Elanco, we are driven by our vision of Food and Companionship Enriching Life and our purpose – all to Go Beyond for Animals, Customers, Society and Our People.

At Elanco, we pride ourselves on fostering a diverse and inclusive work environment. We believe that diversity is the driving force behind innovation, creativity, and overall business success. Here, you’ll be part of a company that values and champions new ways of thinking, work with dynamic individuals, and acquire new skills and experiences that will propel your career to new heights.

Making animals’ lives better makes life better – join our team today!

Your Role:

As the ML Engineer– Drug Discovery & R&D Operations, you will accelerate Elanco’s R&D portfolio by turning proprietary scientific data into predictive models that guide drug discovery and development decisions. You will partner with discovery and development scientists to build machine learning and computer‑vision models on molecular, in vitro, ADMET, and imaging data to support target identification, molecular optimization, digital biomarkers, and endpoint prediction for animal health.

Your Responsibilities:

  • Lead ML/CV work for key R&D programs, partnering with scientists to turn target identification, molecular optimization, in vitro analysis, ADMET, and biomarker questions into modeling problems with clear success criteria.
  • Design, build, and validate advanced ML and computer‑vision models—including CNNs, transformers, graph neural networks, and generative approaches—on multi‑modal data (molecular, assay, imaging, behavioral) to deliver reliable predictions and classifications.
  • Shape data collection, annotation, and synthetic data usage by working with experimental teams on capture protocols, labeling standards, and quality checks; evaluate synthetic data and augmentation to strengthen sparse datasets while managing bias and risk.
  • Operationalize models in R&D workflows by integrating them with tools used by scientists—including building and deploying models in Databricks—and influencing standards for reproducibility, documentation, monitoring, and model governance.
  • Provide technical leadership and mentorship by guiding junior scientists, leading cross‑functional modeling initiatives, and clearly communicating technical concepts and results to diverse stakeholders.

What You Need to Succeed (minimum qualifications):

  • Education & baseline experience: PhD in a quantitative field (e.g., Data Science, Engineering, Physics, Mathematics, Statistics, Bioinformatics, Computational Chemistry), or a Master’s degree with substantial hands‑on ML/CV experience.
  • Applied ML/CV experience: 7+ years of industry experience building and deploying ML and computer‑vision models for scientific problems in biomedical, pharmaceutical, or related domains, with demonstrated impact and close collaboration with wet‑lab or clinical teams.
  • Technical and collaboration skills: Strong Python skills and proficiency with modern ML/CV libraries (e.g., PyTorch, TensorFlow, scikit‑learn, OpenCV), solid understanding of statistics and experimental design, and proven ability to work and communicate effectively in cross‑functional, matrixed environments.

What will give you a competitive edge (preferred qualifications):

  • Domain expertise and impact: Experience in animal health, pharmaceutical, or biotech R&D, with a record of impactful ML/CV applications (e.g., chemoinformatics, bioinformatics, digital biomarkers) shown through projects, publications, or patents.
  • Advanced technical depth and platforms: Hands‑on expertise with methods such as neural networks and generative models, plus practical experience operationalizing models using Databricks and MLOps tools in production‑like or scaled environments.
  • Leadership and influence: Demonstrated success leading cross‑functional teams, mentoring junior scientists, and influencing scientific and technical decisions in complex, global organizations.

Additional Information:

  • Travel: Up to 10%
  • Location: Indianapolis, IN, USA

Don’t meet every single requirement? Studies have shown underrepresented groups are less likely to apply to jobs unless they meet every single qualification. At Elanco we are dedicated to building a diverse and inclusive work environment. If you think you might be a good fit for a role but don't necessarily meet every requirement, we encourage you to apply. You may be the right candidate for this role or other roles!

Elanco Benefits and Perks:

We offer a comprehensive benefits package focusing on financial, physical, and mental well-being while encouraging our employees to pursue our purpose! Some highlights include:

  • Multiple relocation packages
  • Two weeklong shutdowns (mid-summer and year-end) in the US (in addition to PTO)
  • 8-week parental leave
  • 9 Employee Resource Groups
  • Annual bonus offering
  • Flexible work arrangements
  • Up to 6% 401K matching

Elanco is an EEO/Affirmative Action Employer and does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status

Elanco may use automated tools, including AI, to support parts of our recruitment process, such as reviewing applications against job‑related criteria and/or transferrable skills. These tools help ensure a consistent, structured evaluation, but they do not make hiring decisions. All decisions involve a human reviewer. For more information on how we handle personal data, please see our Elanco Workforce Privacy Notice.

#LI-RH1

Elanco is an EEO/Affirmative Action Employer and does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status

Elanco may use automated tools, including AI, to support parts of our recruitment process, such as reviewing applications against job‑related criteria and/or transferrable skills. These tools help ensure a consistent, structured evaluation, but they do not make hiring decisions. All decisions involve a human reviewer. For more information on how we handle personal data, please see our Elanco Workforce Privacy Notice.

Role Details

Company Elanco
Title ML Engineer– Drug Discovery & R&D Operations
Location Indianapolis, IN, US
Category AI/ML Engineer
Experience Mid Level
Salary Not disclosed
Remote No

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 Elanco, 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 (16% of roles) Pytorch (5% of roles) Tensorflow (3% of roles) Transformers (1% of roles)

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. Mid-level AI roles across all categories have a median of $193,200.

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.

Elanco AI Hiring

Elanco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US.

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

Based on 11,866 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 8,943 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Elanco is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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