{"id":1180327,"url":"https://alion.io/job/protolabs-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":179587,"name":"Protolabs","domain":"protolabs.com","url":"https://alion.io/company/protolabs","size_band":"5000+","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":16,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, Pakistan"],"countries":["PK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":35000,"max_usd":88000,"period":"year","method":"role_seniority_country_cell","sample_n":10},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"Karpenter","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Weights & Biases","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-09-24T11:48:35Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-24T20:39:08Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Join the team as our new Senior Machine Learning Engineer - India\nYou will play a key role in advancing Protolabs’ intelligent pricing capabilities by building, maintaining, and improving machine learning models that support real-time quoting for custom-manufactured parts. Working within a complex two-sided marketplace, you will model demand and supply dynamics to improve pricing accuracy and automation, enabling scalable, data-driven decisions even before manufacturing cost inputs are available. You will work closely with engineering, data, product, and domain teams to develop innovative solutions that improve pricing performance and support business growth.\nWhat You'll Do:\nBuild & Evolve Machine Learning Models\nDevelop, improve, and maintain machine learning models that capture demand and supply dynamics within a digital manufacturing marketplace.\nBuild and refine pricing-related models supporting intelligent and automated quoting.\nDevelop models for cost estimation from CAD geometry, demand forecasting, and partner routing probability.\nApply a range of machine learning techniques, including tree-based methods, probabilistic models, and deep learning, to solve both new and existing business challenges.\nTranslate complex marketplace inputs such as part geometry, order history, and partner capacity into meaningful model features.\nDesign & Maintain ML Training and Inference Pipelines\nDesign, build, and maintain reliable machine learning training and inference pipelines on AWS.\nDevelop scalable workflows for experimentation, model training, validation, and deployment.\nApply appropriate model versioning and experiment tracking practices.\nBuild solutions that support reliable production machine learning systems.\nContribute to scaling ML infrastructure as model and business requirements evolve.\nValidate & Improve Model Performance\nConduct offline experiments to evaluate and validate model improvements before deployment.\nApply A/B testing and backtesting approaches to assess model performance.\nMonitor models in production and proactively identify model drift, degradation, or unexpected behaviour.\nSupport monitoring, alerting, and retraining workflows to maintain model performance over time.\nContinuously improve models based on production performance and evolving business requirements.\nCollaborate Across Engineering, Data & Product\nWork closely with ML Engineers, Data Scientists, Product teams, and domain experts in cross-functional teams.\nCollaborate with stakeholders to understand marketplace dynamics and translate business challenges into machine learning solutions.\nWork with real-world, complex, and imperfect datasets to develop practical solutions to ambiguous problems.\nCommunicate model behaviour, results, and technical considerations effectively across technical and non-technical stakeholders.\nContribute to the development of machine learning capabilities that support manufacturing intelligence and business growth.\nMentor & Support Engineering Talent\nMentor and support mid-level and junior engineers within the team.\nShare machine learning engineering practices, technical knowledge, and lessons learned.\nContribute to a collaborative engineering environment focused on continuous learning and improvement.\nStay Current with Machine Learning Innovation\nStay up to date with advancements in machine learning, particularly in pricing, marketplace modelling, and manufacturing intelligence.\nEvaluate emerging approaches and techniques that could improve model performance and business outcomes.\nApply relevant advances pragmatically to production machine learning challenges.\nWhat It Takes:\nTechnical\nProven experience building and deploying machine learning models in production environments.\nStrong coding skills in Python or a similar programming language.\nHands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.\nSolid understanding of supervised and probabilistic modelling, including regression, classification, and uncertainty estimation.\nExperience with feature engineering from structured and/or geometric data.\nHands-on experience with ML pipelines, model versioning, experiment tracking, and MLOps tools such as Weights & Biases, Prefect, Karpenter, or equivalent technologies.\nExperience designing and scaling ML infrastructure for production systems.\nExperience with ML monitoring, alerting, and retraining workflows.\nLeadership & Collaboration\nExperience working effectively in cross-functional teams with ML Engineers, Data Scientists, Product teams, and domain experts.\nStrong communication skills, with the ability to explain complex machine learning models and concepts to non-technical stakeholders.\nAbility to work effectively with ambiguous problems and real-world, messy data.\nExperience mentoring and supporting junior and mid-level engineers.\nAbility to translate complex business and marketplace requirements into practical machine learning solutions.\nMindset\nStrong problem-solving mindset with the ability to work through ambiguous and complex challenges.\nPractical and data-driven approach to developing and improving machine learning solutions.\nCuriosity and willingness to explore emerging machine learning techniques and approaches.\nStrong ownership of model performance, reliability, and production outcomes.\nCollaborative mindset with a willingness to share knowledge and support the development of other engineers.\nAbility to balance experimentation and innovation with the reliability requirements of production systems.\nPreferred Qualifications\nExperience with marketplace or pricing models, including demand modelling, price elasticity, or cost estimation.\nBackground in operations research, econometrics, or supply chain optimisation.\nExperience working with 3D or geometric data, including CAD, point clouds, or mesh processing.\nExperience designing and scaling ML infrastructure for production systems.\nExperience with ML monitoring, alerting, and automated retraining workflows.\nStrong communication skills and experience explaining complex models to non-technical stakeholders.","description_format":"text","description_chars":6099,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Design & Creative","Web Design","UI/UX Design"],"lifecycle":[{"event":"open","at":"2026-09-24T13:41:22Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":30,"reasons":["conf:2","win:early"],"computed_at":"2026-09-24T23:04:00Z"},"pay":null,"html_url":"https://alion.io/job/protolabs-senior-machine-learning-engineer","json_url":"https://alion.io/job/protolabs-senior-machine-learning-engineer.json","meta":{"generated_at":"2026-09-24T23:04:00Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1280,"day_limit":5000,"remaining_today":3720,"minute_limit":60,"resets_at":"2026-09-25T00:00:00Z"}}}