Role Description As a Senior Machine Learning Engineer, you will be responsible for designing, developing, and maintaining high-quality software solutions. You will collaborate with cross-functional teams to understand business requirements and translate them into scalable and efficient software applications. Your role will involve leading technical projects, mentoring junior engineers, and continuously improving software development practices to ensure the delivery of robust and reliable software systems. Responsibilities and Duties - Design, build, and deploy scalable machine learning models and pipelines across the full ML lifecycle; - Develop and optimize data processing workflows using Python, SQL, and distributed frameworks such as PySpark; - Deploy and monitor machine learning models in cloud environments, ensuring performance, reliability, and scalability; - Collaborate with cross-functional teams to translate business problems into data-driven solutions and improve model performance. Qualifications - Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience; - Experience as a Machine Learning Engineer with a focus on deploying and scaling ML systems; - Strong proficiency in Python, SQL, and PySpark for large-scale data processing; - Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost; - Experience designing and managing end-to-end ML pipelines using tools such as MLflow or similar; - Experience deploying models in cloud platforms such as AWS, GCP, Azure, or Databricks; - Strong understanding of MLOps practices including CI/CD, model versioning, and automated retraining; - Experience working with containerized environments and Kubernetes for ML workloads; - Ability to design evaluation metrics and validate model performance effectively; - Strong communication and collaboration skills to work with cross-functional teams and stakeholders. Company Description Encora is the preferred digital engineering and modernization partner of some of the world’s leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora’s technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering. At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

Role Description Fetch is building the future of personalized consumer experiences. We’re looking for a Staff Machine Learning Engineer to serve as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking. In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale. You’ll partner closely with product and cross-functional stakeholders while remaining deeply hands-on in design and development. Responsibilities - Serve as the technical lead for a single ML-focused team, setting direction and raising the bar on engineering quality and system design. - Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases. - Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving. - Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions. - Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability. - Partner closely with product, data, and engineering stakeholders to deliver measurable business impact. - Mentor engineers through hands-on technical guidance, feedback, and example. - Use AI tools to accelerate development and improve system design, including: - Prototyping and validating ideas with LLM tools. - Leveraging AI for code iteration and experimentation. - Using AI assistants for architecture diagramming and design validation. - Exploring LLM-powered features where appropriate. Qualifications - 5+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale. - Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains. - Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure. - Experience deploying ML models into production and operating them at consumer scale. - Demonstrated ownership of complex technical initiatives within a team. - Strong systems design skills with the ability to clearly articulate tradeoffs and implementation decisions. - Experience mentoring engineers and influencing technical standards within a team. - Ability to operate effectively in ambiguous environments and drive projects to completion. Preferred Requirements - Familiarity with LLMs and their application in personalization, feature generation, or search. - Experience with real-time or streaming ML systems. - Exposure to experimentation frameworks (A/B testing) and model performance measurement. - Experience bridging model development with real-time serving systems.

• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold. • Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines. • Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data. • Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. • Be involved in the full project pipeline, from scoping through to results delivery and dissemination. • Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production. • Collaborate cross-functionally to ensure models address real-world drug discovery needs. • Contribute to publications or open-source contributions where relevant.

• Design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics. • Own the full ML pipeline: data preprocessing, feature engineering, model selection, training, evaluation, and deployment into sustainable inference services. • Conduct discovery spikes to validate feasibility and inform go/no-go decisions before committing to full development. • Integrate ML solutions with observability tooling, establishing and maintaining benchmarks to measure improvement and compare approaches. • Build automated, self-sustaining ML pipelines. Models should train, evaluate, and deploy with minimal manual intervention. • Inform build-vs-buy decisions with both technical rigor and business context, understanding when in-house models create competitive advantage vs. when vendor APIs are sufficient. • Collaborate with software engineers, data engineers, and product stakeholders to integrate ML solutions into CompanyCam's platform. • Communicate clearly with non-technical audiences about feasibility, requirements, and trade-offs of proposed solutions.