Machine Learning Engineer Resume Example That Passes ATS Screening
Senior ML engineer resumes often read like research paper bibliographies or, worse, like a laundry list of frameworks somebody pip-installed once. Hiring managers at this level want to see models that made it to production, infrastructure decisions that held up under real traffic, and collaboration with product teams that shaped what got built. This example shows how to present deep technical work without losing the reader in jargon.
Full Resume Sample
Rohan Iyer
Senior Machine Learning Engineer
Professional Summary
Machine learning engineer with 7 years of experience designing, training, and deploying production ML systems across recommendation, search ranking, and natural language processing domains. Currently leading a 4-person ML team responsible for personalization models serving 18M monthly active users at scale. Track record of reducing model inference latency, improving offline-to-online metric alignment, and building ML infrastructure that other teams can ship on without hand-holding.
Experience
Senior Machine Learning Engineer
Spotify · New York, NY · Jan 2022 - Present
- Lead the development and iteration of podcast recommendation models serving 18M+ MAU, improving click-through rate by 12% and listen-through rate by 8% through a two-tower retrieval architecture with real-time user embedding updates
- Designed and deployed a feature store on top of Apache Feast integrated with Spotify's internal data platform, reducing feature engineering time for 3 downstream ML teams from weeks to days and standardizing feature definitions across 40+ production models
- Migrated the podcast ranking pipeline from batch inference (6-hour refresh) to near-real-time serving using TensorFlow Serving behind an internal gRPC gateway, cutting p99 inference latency from 320ms to 45ms
- Mentored 3 junior ML engineers through project scoping, experiment design reviews, and production readiness checklists, with 2 promoted to mid-level within 18 months
- Collaborated with product and editorial teams to define success metrics for algorithmic recommendations, establishing an A/B testing framework that reduced experiment cycle time from 4 weeks to 10 days
Machine Learning Engineer II
Wayfair · Boston, MA · Aug 2019 - Dec 2021
- Built and maintained the visual search model that allowed customers to upload photos and find matching furniture, processing 2.1M image queries per month with a top-5 retrieval accuracy of 74%
- Developed a gradient-boosted ranking model for search results that increased purchase conversion by 6.3% across desktop and mobile, validated through a 3-week holdout experiment with 1.2M users
- Implemented model monitoring dashboards using Datadog and custom Python tooling that tracked prediction drift, feature distribution shifts, and latency regressions across 12 production models
- Reduced training costs by 35% by migrating model training pipelines from on-demand EC2 instances to AWS SageMaker Spot Training with automated checkpointing and resumption
Data Scientist
MassMutual · Springfield, MA · Jun 2017 - Jul 2019
- Developed customer churn prediction models using XGBoost and logistic regression ensembles, identifying at-risk policyholders with 82% precision that enabled the retention team to prioritize outreach and recover an estimated $4.2M in annual premium revenue
- Built an automated underwriting risk scoring pipeline in Python and Airflow that reduced manual review time for low-risk applications by 40%, processing 15,000 applications per quarter
- Partnered with actuarial teams to validate model outputs against historical loss ratios, building trust in ML-driven decision support within a traditionally conservative organization
Education
Master of Science in Computer Science, Machine Learning Specialization — Georgia Institute of Technology, 2017
Bachelor of Science in Electrical Engineering — University of Michigan, 2015 (Minor in Mathematics. Undergraduate research in signal processing and pattern recognition.)
Skills
ML Frameworks & Libraries: PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers, LightGBM, FAISS
ML Infrastructure & Serving: TensorFlow Serving, Triton Inference Server, Apache Feast (feature store), MLflow, AWS SageMaker, Kubeflow, Apache Airflow
Languages & Data: Python, SQL, Scala (Spark), Java, BigQuery, Spark, Kafka
Experimentation & Evaluation: A/B testing design and analysis, Offline evaluation metrics (NDCG, AUC, MAP), Online metric instrumentation, Causal inference basics, Statistical significance testing
Certifications
AWS Certified Machine Learning - Specialty · Google Professional Machine Learning Engineer
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Check Your ATS Score Free →Why This Resume Works
Every model is tied to a product outcome, not just a metric on a test set. Click-through rate up 12%, purchase conversion up 6.3%, $4.2M in recovered premium revenue. Each model Rohan describes is connected to a business result that non-technical stakeholders can understand. Senior ML roles increasingly require the ability to communicate impact beyond offline evaluation metrics, and this resume demonstrates that fluency by pairing technical detail with product language throughout.
Infrastructure contributions signal someone who builds for the team, not just themselves. The feature store that reduced engineering time for 3 downstream teams, the A/B testing framework that cut experiment cycles in half, the model monitoring dashboards tracking 12 production models. These bullets show a senior engineer who invests in shared infrastructure that multiplies the output of the broader ML organization. This is the kind of work that distinguishes a senior individual contributor from a strong mid-level engineer who only ships their own models.
The latency optimization bullet demonstrates production engineering depth. Migrating from batch inference with a 6-hour refresh to near-real-time serving at 45ms p99 latency is a meaningful systems engineering achievement. It shows Rohan understands serving infrastructure, not just model training. Many ML engineers can train a good model in a notebook but struggle with the engineering required to serve it reliably at scale. This bullet addresses that concern directly with specific numbers.
Mentorship and cross-functional collaboration round out the senior profile. Mentoring 3 engineers (2 promoted), collaborating with product and editorial teams on metric definitions, and building trust with actuarial teams at MassMutual - these details show that Rohan operates as a technical leader, not just a technical executor. At the senior level, hiring managers expect you to influence how your team works, not just what it ships. The resume integrates these leadership signals without dedicating a separate section to them.
ATS Keywords for Machine Learning Engineer Resumes
ATS systems scanning Machine Learning Engineer applications look for these terms. The resume above weaves them in naturally rather than listing them outright.
Section-by-Section Writing Tips
Professional Summary
Open with your years of experience and the ML domains you've worked in (recommendations, NLP, computer vision, etc.). State the scale of the systems you own, whether that's user count, query volume, or model count. If you lead a team, mention the size. Avoid listing every framework you've used - save that for the skills section and instead use the summary to communicate your scope of impact.
Experience Section
Every bullet needs two components: what you built technically and what it did for the product or business. A model architecture means nothing without the metric it moved. Equally important, include infrastructure and platform work - feature stores, serving pipelines, monitoring, experiment frameworks. Senior ML engineers are expected to improve the team's velocity, not just their own model performance. Quantify latency, cost savings, and experiment cycle improvements wherever possible.
Skills Section
Separate ML frameworks from infrastructure tools from languages. Hiring managers and recruiters scan for specific framework names (PyTorch, TensorFlow, SageMaker), so list them explicitly rather than grouping everything under 'machine learning.' Include experimentation and evaluation skills as a distinct category since this is a gap in many ML resumes that senior roles require.
Education Section
An MS in computer science or a related field is common at the senior level but not strictly required if you have strong production experience. If your graduate work had a specialization (ML, AI, NLP), name it. Undergraduate degrees in adjacent fields like electrical engineering or mathematics are common and worth listing. Publications and patents can be mentioned briefly under education or in a separate section if you have more than one or two.
Common Machine Learning Engineer Resume Mistakes
Hiring managers reviewing Machine Learning Engineer resumes flag these problems repeatedly. Each one can knock your ATS score or land your application in the rejection pile.
- Listing model architectures and frameworks without connecting them to any business outcome or product metric improvement.
- Focusing entirely on model training and evaluation while ignoring serving infrastructure, monitoring, and production reliability.
- Describing work only in academic language ('novel approach,' 'state-of-the-art performance') rather than production terms that engineering hiring managers respond to.
- Omitting collaboration with product, data, and platform teams, which at the senior level is as important as the technical work itself.
- Failing to mention mentorship, code review, or team enablement activities that are expected at senior levels.
- Treating every position as a list of technologies used rather than a narrative of increasingly complex problems solved.